# Row64
> Row64 is a real-time operational intelligence platform built for live system visibility and zero decision latency in time-sensitive operations.
## Careers
### Low-Level Web Developer
Help us deploy cutting-edge, real-time visual intelligence technology on the web.
Primary experience needed:
- Experience with high-speed web graphics in WebGL and an interest in WebGPU.
- Knowledge of C/C++ and WebAssembly coding and compiling.
- Familiar with web pipelines from databases to server automation.
- Knowledge of lightweight & minimal development using CSS & Flexbox.
### Build Engineer
Manage every phase of Row64's build and release cycles to ensure the timely launch and deployment of software updates.
Primary experience needed:
- Experience with managing software build and release cycles.
- Coordinate engineering to provide stable, high quality and timely software.
- Develop a process that keeps overhead low and turnaround times fast.
- Experience with Github, C/C++, Python, and web deployment.
### GPU Developer
Help us break speed records in Data Science and push the limits of data visualization.
Primary experience needed:
- Experience with GPU coding and optimization.
- Knowledge of both graphics and compute.
- Knowledge of GPU hardware & software architecture.
- Experience with Vulkan, DirectX 12, and Metal graphics APIs.
### Data Engineer
Help Row64 build the future of business intelligence solutions.
Primary experience needed:
- Knowledge of BI tools to communicate insights effectively to stakeholders.
- Proficiency in real-time data pipelines.
- Experience with designing, implementing, and optimizing data warehouses
- Familiar with ETL processes for data ingestion.
- Understanding of data modeling concepts
### Enterprise Account Executive
Help expand our footprint via a consultative, technical approach to enterprise sales by combining deep knowledge of the customer's problems with a knack for building trust and closing strategic deals.
Primary experience needed:
- Proven success in closing complex deals with enterprise accounts.
- Experience as a founding enterprise sales executive.
- Deep understanding of the modern data pipeline and customer needs
- Top-notch communication, negotiation, and storytelling skills.
### Solutions Engineer
Help guide clients through our platform's integration and optimization within their complex data environments. As a trusted advisor, you will bring our modern data science toolset to solve clients’ most difficult challenges.
Primary experience needed:
- Strong understanding of real-time data pipelines and data visualization workflows
- Ability to understand business requirements and translate them into technical solutions
- Committed to delivering exceptional customer experiences
- Excellent communication skills
### Low-Level Developer
Help us build the next generation of groundbreaking data tools in Row64.
Primary experience needed:
- Experience with low-level systems coding in C/C++.
- Interest in hardware-focused development.
- Knowledge of software optimization, compilers, and high-speed byte manipulation.
- Knowledge or interest in interfacing Python with C/C++.
## Resources
### Word Clouds in Realtime
### How to reorder columns inside of a dataframe using Pandas and Row64
### Effect of AWS on Amazon's Operating Income
Animated Bar Chart depicting the impact of AWS.
[Download the project](https://app.row64.com/Amazon.r64)
### Radar
Here is an example of a Radar in Row 64 and how to access it.
Click Recipes under the DataFrame tab.
Click Charts.
Click on Radar.
Click Run.
### How to expand data science python skills to everyone with Row64?
### Supply Chain Guided Tour
Supply chain data delays are costing you money. In this 90-second example of Row64’s capabilities, we show you how to close the gap. Click on the Get Started button below →
To get a personalized demo tailored to your operations, contact\:\
[anthony.duca\@row64.com](mailto:anthony.duca@row64.com)
### Real vs. Normal Disposable Income
Animated Line Chart depicting the relationship between Disposable Income value and Value Adjusted for Inflation.
[Download the project](https://app.row64.com/Inflation-Disposable_Income.r64)
### The Decline of CA's 10 Largest Water Resources
Animated Bar Chart depicting the rapid decline of water resources in the state of CA.
[Download the project](https://app.row64.com/CA.r64)
### 30 Million Rows
[Test Load Speed and Filtering - 30 million rows](/f/c0zz1zmm/30m-demo.zip)
### Connecting Dashboard to a Database
### ETL Techniques: How to group and sum data inside of a dataframe using Pandas and Row64?
### Horizontal Bar Chart
Here is an example of a Horizontal Bar Chart in Row 64 and how to access it.
Click Recipes under the DataFrame tab.
Click Charts.
Click on Horizontal Bar.
Click Run.
### A1 Notation
### Python Radial Charts Tutorial Circular Bar Chart, Donut Chart, Pie Chart and Radar Plots in Row64
### Racing Bar Chart Animations (re-ordering bars)
### Spreadsheet Formulas
[Pokémon Go Geoanalysis](/f/l11mj5nx/formulas.zip)
### 100 Million Rows
[Load Speed with 100 million records - demo](/f/xd7owe8o/100m-record-project.zip)
### Bubble Plot
Here is an example of a Bubble Plots in Row 64 and how to access it.
Click Recipes under the DataFrame tab.
Click Charts.
Click on Bubble Plot.
Click Run.
### Stream Graph
Here is an example of a Stream Graph in Row 64 and how to access it.
Click Recipes under the DataFrame tab.
Click Charts.
Click on Stream Graph.
Click Run.
### How to load, combine and merge CSVs into a dataframe using Pandas, Row64, and the command prompt?
### Example Animation: Racing Line Chart
### ChatGPT as a programming assistant.
### Example Animation: Reordering Bar Charts
### Bar Chart
Here is an example of a Bar Chart in Row 64 and how to access it.
Click Recipes under the DataFrame tab.
Click Charts.
Click on Bar.
Click Run.
### Row64 Cloud & Server 101
### State of Data Software Survey 2024
Despite the vast advancements in data science, the accessibility of data software is still opaque. While Excel continues to be the most popular data analysis software of the last twenty years, a growing landscape of ETL, data manipulation, and data visualization tools has created diverse workflows across a range of software. Despite this, many data pipelines and workflows are siloed, and more information is required to understand what other business intelligence software and scripting languages are used.
This lack of visibility results in a massive underestimation of lost or wasted time, underused computing resources, and unnecessarily complex onboarding processes. In effect, businesses choose from options they are aware of, accepting the pitfalls as a cost of working with data.
To illuminate this issue further, we surveyed 350 data analysts. Some key findings are listed below:
- **Prevalence of Excel and Power BI**: Microsoft Excel remains highly popular among data analysts, used by 65% of survey respondents. Power BI is also significant, used by 46%.
- **Issues with Data Dashboards**: The primary concerns with data dashboards are lack of customization (54% of respondents), missing real-time capabilities (44%), and difficulties in onboarding new employees (41%).
- **Data Software Lag:** Significant delays are reported due to large data sets, with 83% of respondents experiencing delays with 1 million or more data records. This lag can extend project times significantly, often requiring 1-3 hours to convert raw data into usable insights, and even longer for some companies.
- **Necessity of Python Skills:** Python is considered essential by 78% of analysts for modern data tasks, but 80% report a lack of Python skills as a significant barrier.
- **Data Visualization Challenges**: Although crucial, 88% of analysts find creating compelling data visualizations challenging, impacting the overall effectiveness of data insights.
- **Importance of Large Language Models** (LLMs): LLMs are viewed as important by 90% of respondents, indicating a trend toward integrating AI and machine learning in data analysis tools.
Complete the form on this page if you’d like to download a full PDF version of our data analyst survey, including all results broken out by respondent count and access to all 17 questions.
### Geo Analysis
[Next gen graphics (requires graphics card)](/f/2fngf5fc/geo-demo.zip)
### GPU Charting
### Apple Sales By Product Type
Animated Pie Chart Showing The Impact of Apple Product Lines On Total Revenue Over Time.
[Download the project](https://app.row64.com/Apple.r64)
### Example Animation: Animated Donut Charts
### Fast Data Science Recipies
### Row64 Dashboard Performance: Contiguous Memory
### Geo Big Data Analysis
[44 Million Cell Towers (requires graphics card)](/f/xu3wd4ro/geo-bigdata.zip)
### How to delete or remove columns inside of a dataframe using Pandas and Row64
### Racing Line Chart Animations and Keyframe editing
### Dash-Sql And Join
### Top 15 Subreddit Subscriber Growth
Racing Line Chart depicting subscriber growth of top 15 subreddit subgroups
[Download the project](https://app.row64.com/Reddit.r64)
### Custom Recipe Creation Sharing Python Scripts with Team as a Data Scientist
### Direct Python Pandas Dataframe Manipulation without any code
### How to change a single value or cell inside a dataframe using Pandas and Row64
### Recipe Manager, Dependencies, PIP, Debugging: Python Libraries installed on demand, Debug Tools
### GPU Financial Modeling
### Data Mining
### Date Charting Features
### GPU Data Visualization
### GPU GEO Analysis
### GPU Dataframe Operations
### Row64 WSL2 Dashboards
### Impact of COVID-19 on US Domestic Box Office
Top 15 Movie Studio Performance Total Gross Box Office (Last Decade)\]
[Download the project](https://app.row64.com/BoxOffice.r64)
### Logistic Regression with Row64
### Word Cloud
Here is an example of a Word Cloud in Row 64 and how to access it.
Click Recipes under the DataFrame tab.
Click Charts.
Click on Word Cloud.
Click Run.
### Tree Map
Here is an example of a Tree Map in Row 64 and how to access it.
Click Recipes under the DataFrame tab.
Click Charts.
Click on Tree Map.
Click Run.
### The Future of Data Visualization
### Cloropleth
Here is an example of a Choropleth in Row 64 and how to access it.
Click Recipes under the DataFrame tab.
Click Charts.
Click on Choropleth.
Click Run.
### GPU Data Cleaning
### Heat Map
Here is an example of a Heat Map in Row 64 and how to access it.
Click Recipes under the DataFrame tab.
Click Charts.
Click on Heatmap.
Click Run.
### ETL Techniques: Extract data from a database and import it into dataframe using Pandas and Row64?
### Density Plots
Here is an example of a Density Plot in Row 64 and how to access it.
Click Recipes under the DataFrame tab.
Click Charts.
Scroll down until you find Density Plot and click on it.
Click Run.
### Build custom Row64 Data Science Recipes and Share Python code with your team Part #1
### How to sort and deduplicate/dedupe columns inside of a dataframe using Pandas and Row64
### Producer Price Index YoY Change
Animated bar chart showing the rampant growing cost of goods and services in the United States.
[Download the project](https://app.row64.com/Inflation-PPI.r64)
### Big Data Text Search
[Product Review Example](/f/1repvuks/bigtext-demo.zip)
### Line Plots
Here is an example of a Line Plot in Row 64 and how to access it.
Click Recipes under the DataFrame tab.
Click Charts.
Click on Line Plot.
Click Run.
### Example Animation: Animated Line and Bar Charts
### Cleaning Time Data
### 10 Million Rows
[10 Million Rows Demo Zip](/f/1p55dy2c/10m-demo.zip)
### How to do a cohort analysis and churn analysis using Python and Row64
### Row64 Dashboard Training - Overview
### Seaborn PairPlot and Using Hue Parameter
### Gender Pay Gap Analysis
Racing line chart depicting gender pay gap over time.
[Download the project](https://app.row64.com/GenderPay.r64)
### AECOM Museum Attendance 2012-2021
Animated bar chart depicting AECOM's top museum attendance
[Download the project](https://app.row64.com/Museum.r64)
### Dashboard-Sentiment
### GPU Spreadsheet
### Use premade data science with templates learn Python Pandas and Data Viz
### How much does your state contribute to federal taxes?
Animated Bar Chart depicting states contribution to federal taxes.
[Download the project](https://app.row64.com/Taxes.r64)
### Menu Bar Tabs (Pandas Dataframe, Spreadsheet, and Notebook)
### Disney Parks Annual Attendance
Animated Bar Chart depicting Disney Park Attendance over time
[Download the project](https://app.row64.com/Disney.r64)
### Surface Charts
Here is an example of a Surface Chart in Row 64 and how to access it.
Click Recipes under the DataFrame tab.
Click Charts.
Click on Surface.
Click Run.
### Recover the margin already sitting on a loss
Four levers, one number. Adjust to the prospect's actuals and watch the recoverable value assemble in real time. Every default is benchmarked and cited below.
You don't pay Row64 from the operational budget — **you pay from the money we recover for you.** The output below is annual recoverable value set against the Row64 cost: net first-year gain, payback in weeks, and the return multiple. If it doesn't fund itself inside the first quarter, the assumptions get revisited with the prospect — not the other way around.
Template / balanced Carrier / OTR fleet DC / yard-heavy Dealer / Fleet
00
Fleet & operating scale
Trucks / power units in fleetTotal tracked units under management
Avg loads / stops per truck / weekLoad + unload events generating dwell exposure
Operating weeks / yearStandard 50; adjust for seasonal fleets
01
Driver & detention time
$0
Stops that run into detention (%)ATRI 2024: 39.3% of all stops exceed the 2-hr grace
Recoverable detention hrs / eventBillable slice beyond grace; OOIDA: 70% wait 3+ hrs
Detention rate ($/hr)ATRI avg fee charged $63.71; op cost $90.89/hr
Recovery lift with live timestamps (%)Real-time gate + dock proof; <50% collected today
02
Dwell & yard time
$0
Minutes lost / load to yard frictionDataDocks: ~20 min hunting a trailer; 10+ min gate
Fully-loaded labor rate ($/hr)2026 DC fully loaded $22–28 on a $20 wage
Friction removed by live yard view (%)Live view kills the search, not the physical move
03
Labor cost recovery
$0
People reconciling data by hand (FTE)TMS · WMS · YMS · telematics stitched manually
Fully-loaded cost / FTE ($/yr)Salary + benefits + overhead
Time spent hunting data (%)Industry: 60–70% of analyst time is data gathering
Reclaimed by one live picture (%)Recovered as analysis, not reconciliation
04
Insurance premium reduction
$0
Annual commercial auto premium ($)Insurance = 8–15% of fleet operating budget
Premium reduction from verified telematics (%)Vetted range 10–25%; 12% is the conservative floor
Annual recoverable value
$0
–
Conservative Base case Upside
Row64 platform (Studio + Server)$0
One-time onboarding$0
Net first-year gain$0
Payback period–
Return multiple (yr 1)–
3-year net value$0
–
Row64 annual platform ($)
Onboarding ($)
## Training
### Widgets
##### Overview:
An advanced dashboard with 2 data frames of input and many widgets. Create many kinds of widgets, from sliders and combo boxes to date ranges. Example content is here: [https://drive.google.com/file/d/1iFarBWURjIa1L3k1QUhKcsihKg4fW4YP/view?usp=drive\_link](https://drive.google.com/file/d/1iFarBWURjIa1L3k1QUhKcsihKg4fW4YP/view?usp=drive_link)
### Connect Database and Dashboard
##### Overview:
This video covers using Python to easily connect Row64 dashboards to a database source. The primary tool used is row64tools. More details can be found at: [https://pypi.org/project/row64tools/](https://pypi.org/project/row64tools/) [https://github.com/Row64/row64tools.](https://github.com/Row64/row64tools) We also cover some of the performance benefits of .ramdb files, ByteStream, and contiguous
### Business Process Diagram
##### Overview:
Dashboard training with a simple BPMN-style business process diagram. Interactive diagrams respond to cross-filtering. Example content is here: [https://drive.google.com/file/d/1l7qNdVsiVWhqGV4cUoCvmCSgU8B1hP2U/view?usp=drive\_link](https://drive.google.com/file/d/1l7qNdVsiVWhqGV4cUoCvmCSgU8B1hP2U/view?usp=drive_link)
### Expenses
##### Overview:
A simple dashboard training of using spreadsheets for high level analysis within expense records. Example content is here: [https://drive.google.com/file/d/1zrikq4J6HPiCP5git1YqICXI0ePqLHQZ/view?usp=drive\_link](https://drive.google.com/file/d/1zrikq4J6HPiCP5git1YqICXI0ePqLHQZ/view?usp=drive_link)
### Profit
##### Overview:
Dashboard training to model company profit using a high-level spreadsheet (with over 1M records). It has 2 data frames as input (Sales & Expenses) that are linked together with advanced cross-filtering. Example content is here: [https://drive.google.com/file/d/1TyD4q3XwqgxTrBISL\_T6nuA\_SfZET9j-/view?usp=drive\_link](https://drive.google.com/file/d/1TyD4q3XwqgxTrBISL_T6nuA_SfZET9j-/view?usp=drive_link)
### Dense Sales Data
##### Overview:
Dashboard training with 1 Million records, so a good speed and scale test. Shows cross-filtering, text search and advanced record inspection. Example content is here: [https://drive.google.com/file/d/1BXGVmH9qe0Lh3Il8aksChFmH3BEYHR7P/view?usp=drive\_link](https://drive.google.com/file/d/1BXGVmH9qe0Lh3Il8aksChFmH3BEYHR7P/view?usp=drive_link)
### R Squared
### Cloud and Server Intro
##### Overview:
This video gives an overview of how Row64 is set up to run on the cloud and server. We try to address both the questions a Windows user or a Linux expert would have when they first started using Row64 dashboards.
### WSL2
##### Overview:
WSL2 is a faster and more Windows-esque alternative to VirtualBox. It's great for using the Row64 Server locally on your Windows machines and can be up to 10X faster and 10X simpler. In this video, we look at some of the neat features of WSL2 combined with Row64
### Sentiment
##### Overview:
Dashboard training using word clouds as a sentiment interface and analysis tool. Interactive sliders for filtering sentiment, an example of advanced cross-filtering Example content is here: [https://drive.google.com/file/d/1600BrN5LXgajn\_KCDpV4-vz5tGiNSAcz/view?usp=drive\_link](https://drive.google.com/file/d/1600BrN5LXgajn_KCDpV4-vz5tGiNSAcz/view?usp=drive_link)
### Getting Started
##### Overview:
This is a simple dashboard training to get started with Row64 Dashboards. It Includes chart creation and simple cross-filtering. Example content can be downloaded here: [https://drive.google.com/file/d/1v1JSN1-0LIILoL6FOhy8yyd-cpt7D10p/view?usp=drive\_link](https://drive.google.com/file/d/1v1JSN1-0LIILoL6FOhy8yyd-cpt7D10p/view?usp=drive_link)
### Dashboard Basics
##### Overview:
A big picture overview on making Row64 dashboards. We start with the simple steps to make cross-filtering work perfectly on your dashboard. Then we get into the details of more complex special cases with Recipes, Formulas, and Charts.
### Credit Card Fraud Detection
##### Overview
This training video provides an overview of setting up a credit card fraud detection use case in Row64. It demonstrates how to transform data using Row64 data functions, build an interactive dashboard through the drag-and-drop interface, and enable the streaming service on the Row64 server for real-time analysis and monitoring.
[Click here](https://drive.google.com/file/d/144zoS2zjcv7qnw9PUJVovxn6Kw0P9ycK/view?usp=sharing) to access the source content needed to build this example.
### Seasonal (Advanced)
Overview:
Advanced dashboard training with advanced cross-filtering, formula columns & conditional formatting. Pulls in raw data and uses formulas for seasonal comparison, blended by slider. Example content is here: [https://drive.google.com/file/d/1x4IDTswhOX4j0zLKZEw5Qnq640tz4AUG/view?usp=drive\_link](https://drive.google.com/file/d/1x4IDTswhOX4j0zLKZEw5Qnq640tz4AUG/view?usp=drive_link)
### Weather
Overview:
Dashboard training with animated choropleth chart of changing weather patterns over time. In the browser, move the mouse near the bottom of the map, and you’ll see a play bar pop up. Example content is here: [https://drive.google.com/file/d/1WaRTBAP\_aBSbOb7FA-hwKPDQlDAqBFoz/view?usp=drive\_link](https://drive.google.com/file/d/1WaRTBAP_aBSbOb7FA-hwKPDQlDAqBFoz/view?usp=drive_link)
### Census
##### Overview:
Dashboard training with interactive slider filtering of Census Data. A good example of using layers to build a polished interactive map. Example content is here: [https://drive.google.com/file/d/142xVu5yFYavN\_EBTtA786tY2IdzKpxn9/view?usp=drive\_link](https://drive.google.com/file/d/142xVu5yFYavN_EBTtA786tY2IdzKpxn9/view?usp=drive_link)
### Cell Towers
##### Overview:
Dashboard training with speed and scale test of 2M cell towers Cross filtering of large dataset while viewed on a map. Example content is here: [https://drive.google.com/file/d/1eisJsXPxFQgQg8smnthNvrRo10MJy07n/view?usp=drive\_link](https://drive.google.com/file/d/1eisJsXPxFQgQg8smnthNvrRo10MJy07n/view?usp=drive_link)
### Interactive Bubble Charts
##### Overview:
Dashboard training using multiple cross-filters connecting to a bubble chart. This is an excellent example of interactive slider use. Example content is here: [https://drive.google.com/file/d/1EnMkvRodEjIscr1pGa9oLtkc3Xl9xy1G/view?usp=drive\_link](https://drive.google.com/file/d/1EnMkvRodEjIscr1pGa9oLtkc3Xl9xy1G/view?usp=drive_link)
### Game Level Map
##### Overview:
Dashboard training with game-level play-testing data is being filtered over time. An example is using many layers to build an interactive dashboard diagram. Example content is here: [https://drive.google.com/file/d/167W1LJw3FcErz-hxC4-xbLsOTetXS06m/view?usp=drive\_link](https://drive.google.com/file/d/167W1LJw3FcErz-hxC4-xbLsOTetXS06m/view?usp=drive_link)
### Amazon
##### Overview:
Dashboard training for filtering large volumes of paragraph text. Double-click (in the browser) on the text column for a paragraph text window to pop up. Example content is here: [https://drive.google.com/file/d/1W\_dQ9upqbkfG7qckQXJONBiDxT1KkyMp/view?usp=drive\_link](https://drive.google.com/file/d/1W_dQ9upqbkfG7qckQXJONBiDxT1KkyMp/view?usp=drive_link)
### Date Grouping
##### Overview:
Dashboard training using dates, cross-filtering and making formula columns Example content is here: [https://drive.google.com/file/d/1LMQMB7WW-Tzg-nd9OqfKAWjz-iN-f-9n/view?usp=drive\_link](https://drive.google.com/file/d/1LMQMB7WW-Tzg-nd9OqfKAWjz-iN-f-9n/view?usp=drive_link)
## Blog
> A collection of posts that share insights, ideas, and updates to the industry.
### Version 3.4 Highlights
**Record-breaking performance:** Work at fine-grain detail directly in the browser with up to 1 billion records interactively.
**Next-Gen Visual Analytics**: Explore vast, diverse data sets with automatic cross-filtering, data frames supporting complex data types (images, text, etc.), advanced geo-analysis with line networks, data connections, and more in real-time.
**Predictable costs:** scale up to 1 Billion records with a single instance server costing under $5k.
**Simplified toolchain:** You can quickly work across multiple large data sets in one application (ingest, analyze, visualize), reducing the prep time required from high-value programming resources.
**Audit ready:** all the tools required to pass compliance and security audits, including HTTPS, Auth, and fine-grain user roles and permissions.
**Accessible to everyone**: data application creation, debugging, and deployment for technical and non-technical users has never been easier leveraging standards like Excel formulas and a purpose-built IDE.
**Robust API:** opens the door to multi-model input and AI automation
**Access anywhere:** Mobile and Tablet Support
### How Row64 Aids Fraud Prevention
##### Financial fraud is a huge problem. Legacy data systems aren’t keeping up.
The costs of fraud to financial institutions are staggering. A [report](https://www.globenewswire.com/news-release/2025/08/18/3134696/0/en/Fraud-to-Cost-Financial-Institutions-58-3-Billion-by-2030-Globally-as-Synthetic-Identities-Threaten-Fraud-Tidal-Wave.html?print=1%5C&utm_source=chatgpt.com) from Juniper Research estimates that global fraud losses to financial institutions could hit US$58.3 billion by 2030, up from about US$23 billion in 2025. Fraud doesn’t just mean losing money to criminals — it also means fighting disputes, as well as maintaining regulatory compliance and customer goodwill.
Put simply, given the direct and indirect costs, fraud prevention continues to grow in importance, and legacy data systems aren’t equipped to handle it.
##### Legacy data systems can’t keep up.
Fraud is evolving rapidly, yet the fraud prevention systems are still playing catch-up. Some of the key pressure points:
##### Speed and scale
Attackers are using bots, automation, and synthetic identities at scale. Yet a 2025 [LexisNexis study](https://risk.lexisnexis.com/global/en/about-us/press-room/press-release/20250910-fraud-multiplier?utm_source=chatgpt.com) found that 44% of North American financial institutions still rely on manual fraud-detection processes. When millions of transactions, account-creation events, logins, and access attempts flow through an institution's systems, manual or batch detection becomes a liability.
##### Visibility and context
Many fraudulent events span multiple channels — including new account creation, loan origination, transaction behavior, and login anomalies — yet only some institutions track fraud comprehensively across all touchpoints. Without real-time access to data across the organization, subtle fraud patterns can go undetected until damage is done.
---
##### How Row64 helps institutions get ahead
Given the stakes, financial institutions can no longer afford to rely on systems that can’t process information at the speed and scale of today’s criminals. That’s why Row64’s CPU/GPU-accelerated, real-time operational intelligence platform is a game-changer for fraud teams in financial institutions. It provides real-time operational intelligence, acting as a command-and-control center for fraud detection teams by aggregating and correlating data from multiple sources—including data warehouses, streaming data, real-time data, and AI pipelines—so fraud teams can understand and act on events at a moment’s notice.
##### CPU/GPU-accelerated performance
Streaming updates at sub-millisecond speeds, visually exploring hundreds of millions of records (including both structured and unstructured data) in real-time demand a new level of performance. Row64’s CPU/GPU-accelerated architecture dramatically reduces latency, enabling fraud teams to bring together vast sources of information to explore, identify correlations, and flag issues that legacy systems simply cannot match.
##### Streaming data and interactive visualization at scale
Instead of waiting for batch fraud reports or isolated alerts, Row64 can stream aggregate data from batch, streaming, and real-time data sources, letting teams instantly explore millions of records across numerous touchpoints via GPU-accelerated dashboards. This means institutions can spot emerging threat patterns *as they happen*, not after the fact.
##### Cross filtering for correlated data discovery
Humans are great at pattern recognition — but only if the data is surfaced in meaningful ways. Row64’s cross-filtering functionality enables fraud analysts to drill down from high-level dashboards into clusters of correlated information, e.g., multiple accounts created from the same device, followed by unusual transaction volumes or geographic locations. That kind of granularity helps spot bot attacks, account takeovers, and other incidents — before they escalate.
##### Cross-channel visibility
Because fraud doesn’t happen in isolation, Row64 enables teams to bring together data from multiple sources and streams — application data, transaction data, login/behavioural data, device & network signals — into a unified analysis interface where teams can detect patterns spanning the institution.
##### Putting humans in the loop
Institutions are leveraging artificial intelligence (AI) to help address this significant challenge. While AI automates parts of the process, institutions require a human in the loop to make final decisions when billions of dollars are at stake. Row64's precision and accuracy enable humans to quickly validate information, making it an indispensable part of the fraud detection pipeline.
##### Delivering ROI
Fraud isn’t simply a cost of doing business any longer — it’s a significant drag on profit, trust, and regulatory compliance. With every US$1 of fraud now costing financial institutions **~US$4–5** in total impact (and growing), the imperative for faster, more intelligent detection is greater than ever.
If a team reduces add-on costs by even $1 or 20-25% with a real-time operational intelligence platform like Row64, that could save an institution millions in direct costs while helping prevent customer churn.
##### From Data Visualization to Real-Time Operational Intelligence
Legacy systems that rely on manual review, siloed data, and batch processing can’t keep up with the speed, scale, and complexity of modern attacks. Row64 changes the equation with a real-time, CPU/GPU-accelerated platform that transforms data visualization into a real-time command and control center, giving teams a single source for all operational data.
### What Is Visual Analytics and How Can It Help Your Business?
The word "visual analytics" has been gaining popularity, both in data science and business communities, for its increasing usefulness in understanding and relaying information. While it's closely related to data visualization as a concept, visual analytics is slightly different in that visual representation of data isn't just the final product but an integral part of the entire process.
Put simply, visual analytics is the means of exploring and analyzing data sets entirely within a visual interface. Rather than using solely numbers and text to represent information, visual analytics uses symbols, colors and even animations to manipulate and display data-based information.
Visual analytics can be as simple as color coding text or cells in a spreadsheet, or as complex as rendering a 3D model of the world. Oftentimes visual analytics takes the form of an interactive data dashboard. It's truly whatever helps you and your audience more quickly and clearly understand and convey the desired information.
#### Why Visual Analytics Can Help Your Business
Visual analytics helps your business by showing trends and other relevant data-based insights as quickly and clearly as possible.
It's important to remember that despite our highly evolved forms of verbal and written communication, human brains evolved to process visual information roughly [60,000 times faster than text.](https://blog.csgsolutions.com/15-statistics-prove-power-data-visualization) Leveraging this fact by converting information into easily digestible visual form can help you process and communicate information faster than the competition—saving your business time and money.
If you don't think your business needs visual analytics consider the following statistics:
- [The Wharton School of Business found that data visualization](https://www.amanet.org/articles/using-visual-language-to-create-the-case-for-change/) could shorten meetings by 23%.
- According to Bain & Co, companies with advanced analytics capabilities are [5x more likely to make faster](https://www.bain.com/insights/big_data_the_organizational_challenge) decisions than their peers.
- An estimated [73% of data goes unused for analytics purposes](https://www.forrester.com/blogs/hadoop-is-datas-darling-for-a-reason/). Around [63% of companies](https://findstack.com/big-data-statistics/) can't even gather insights from big data.
- Poor and unused data costs US businesses roughly [$3.1 trillion a year](https://www.informationweek.com/big-data-analytics/big-data-analytics-sales-will-reach-187-billion-by-2019).
Put it all together and what becomes obvious is that better data visualization and better visual analytics are not only a business improvement, but a business necessity— uncovering hidden earning potential while streamlining insights and key benchmarks.
It's truly the miracle tool of modern times.
#### What Type of Questions Does Visual Analytics Seek To Answer?
Visual analytics is used to answer virtually any question where data intersects with actionable insight. Typically these will be uniform data sets with finite values, such as sales or marketing data—as opposed to something open ended like names, or survey responses.
A couple types of questions that visual analytics can answer:
##### Supply Chain Visibility
Visual analytics can help store or warehouse managers easily oversee inventory by location, or product type. Color codes can help show high and low inventory levels, while map overlays can help show exactly where those items are located, merging disparate data sources into one stream.
*A visual distribution of store inventory by items in store (source: Qlik)*
##### Marketing/Customer Acquisition
Visual analytics is great for boosting ROI on marketing campaigns, by illuminating data of each customer phase and their life cycle. Quickly see customer drop off points, geographic or demographic trends, or what campaigns are delivering the highest engagement, all without needing to search through large sets of numbers.
##### Financial Analysis
One of the main benefits of visual analytics is being able to help easily show data distribution. In the case of financial analysts, bar charts or scatter plots can show breakdowns of individual loans or loan applicants within a larger bundled portfolio.This is particularly useful in a portfolio with very diverse inputs as the distribution of risk and margin provide a much clearer picture than a mean or median would with only one value.
#### Key Methods to Quickly Increase The Effectiveness of Your Visual Analytics
One of the most effective techniques in visual analytics and data visualization is the use of "pre-attentive attributes". This refers to information we process immediately without needing to think to understand it.
The use of pre-attentive attributes is so common, you come across them everyday without realizing. A red octagon sign means stop. Green stock tickers mean prices are up. Multiple chili peppers on a menu denotes spicy.
Using pre-attentive attributes is extremely effective for helping to streamline information, and is incredibly useful for business. Here are the main ones:
- **Shapes**. Often used in visual analytics, with different symbols representing different figures. Think circles representing cities on a map, and stars representing state capitals.
- **Size, Length and Width**. Using the example above, large cities on maps tend to be larger circles than small circles. In the instance of a bar chart, length and width is always associated with a corresponding change in quantity.
- **Color**. Referred to both in hue and intensity, color can quickly show what direction something is moving (often spreadsheets have negative numbers in red) and by how much, with the saturation of a color on a heatmap or chart often corresponding to a numeric threshold.
- **Position and Grouping**. By clustering or grouping data points, you can make it easy to instantly identify data by type or source. A venn diagram would be the most basic of these.
*The scatter plot above uses color, size and distribution to show the frequency and amount of tips relative to bill totals. (Source: Row64)*
#### Visual Analytics by Time
Within visual analytics, there's both descriptive analytics — which attempts to find the cause behind something that's already occurred, and prescriptive analytics and predictive analytics, which aim to create action plans based on the insights from past data.
The main difference between prescriptive analytics and predictive analytics is that prescriptive analytics is based on creating a solution for the present state (for instance a reactive marketing campaign operating in real time) and predictive analytics attempts to create a strategy for an event that has yet to occur (for instance a product to market launch).
#### Interactive vs Static Visual Analytics
One of the most popular developments in visual analytics is the use of interactive data visualizations. Like their name suggests, these allow the user to drill down, manipulate or segment their data in real time, all within a visual interface.
Examples of this include clicking on a state or county to zoom in on a map, highlight a section of a pie chart to reveal its sub sections, or highlight specific cells by data type or source. Interactive visual analytics are very common on data dashboards.
#### Best Tools for Visual Analytics
While visual analytics can come from anywhere or be made by anyone, they tend to be associated with software that does much of the work for the user. Many of these programs provide much of the same output, with the main factors being their integration with the cloud, the level of detail in their graphics, and how they are used across a team.
While both visual analytics and data visualization are growing fields,
1. [Tableau](https://www.tableau.com/products/desktop). Tableau is arguably the name in visual analytics, with several courses being taught dedicated to its specific software. Tableau can create most any type of data visualization — including scatter plots, Ganntt charts, tables, and maps — and can work both for data visualization, as well as an interactive data dashboard - including use as a CRM.
2. [Qlik](https://www.qlik.com/us/data-visualization/visual-analytics). One of the newer visual analytics platforms out there, Qlik offers less complex data visualization options than Tableau, but is well suited for a business professional that wants a streamlined platform to quickly view KPI progress or inventory levels. It also requires less in-depth training than Tableau.
3. [Sas Viya](https://www.sas.com/en_us/home.html). Sas Viya is great for users who aren't interested in creating custom data visualizations but want a suite of AI, analytics and data management, all presented to them in an easy to view dashboard. Sas Viya is cloud-native making it a great option for enterprise business looking to unify insights across teams.
4. [SiSense Fusion](https://www.sisense.com/get/demo/). SiSense is one of the newer companies in the visual analytics space, and is making a name for itself as a no-code predictive analytics platform. Their cloud based data platform is designed for users with no data science background who want to easily look at the same data insights.
5. **Row64.** What sets Row64 apart from other platforms is the speed and scale of its software. By borrowing a page from the world of gaming and 3D modeling, Row64 harnesses the power of your computer's GPU to be able to render millions of points in real time. While not a true data dashboard, the ability to render visualizations from the largest of spreadsheets, Row64 is one of the only platforms equipped to handle the size of modern data—with raytracing capabilities far beyond what anyone else is offering.
### Closing the Detection Gap in Circular Supply Chains
Almost every supply chain runs on three systems\: ERP, TMS, and WMS. Procurement buys through one, transportation moves through another, and the warehouse receives through a third. Then, the cycle reverses and runs again.
While none of these systems is subordinate to the others, none of them talks to the others in real time. That's not a flaw in any single system. It's the structural condition of how supply chains operate today, and it creates a gap that slowly erodes margins.
#### Systems Out of Sync
Supply chains operate in a loop. Procurement issues a purchase order, and that PO is pushed to transportation, which now has to manage the logistics of moving the product. A carrier brings it in. On delivery, the item exits the transportation system and is received into the warehouse. Once received, the inventory hits the books in the ERP, and it becomes sellable. A sale of that item pulls the same inventory back out of the warehouse, into outbound transportation, and updates the ERP again. The circle keeps moving, continuously, in both directions.
Each piece of that loop is essential. Yet, each system updates on its own cadence. For instance, ERP might refresh every 30–60 minutes, but WMS updates every 15 minutes, and TMS shows events every 30–90 minutes (or only when events occur).\
\
Decisions are made based on whichever screen an executive happens to be looking at, and that screen is almost always stale relative to at least one other system in the loop. That gap between what's live and what's on screen is the Detection Gap, and it poses a real risk to companies.
Here’s how\:
Think of it like the retail aisle problem\: the app says the item is on aisle A4. You walk over, and it's gone. The system hasn't caught up to its own feeds. Now scale that to a $200K order. A CEO looks at the ERP, sees 1,000 units on hand, authorizes a 20% discount to close the deal, and Sales books it to hit the month. Fulfillment goes to ship, but only 750 units physically exist. The order was priced and promised on bad intel, and now it can't be filled.
When that happens, the blame usually lands on shipping. But shipping can only ship what they can transact. The bottleneck isn't the warehouse team — it's the data they're working from.
There's a second layer riding on top of this same loop\: total landed cost. The ERP carries the number that answers "was our margin 7% or 12%?" That number erodes through transportation spend, internal labor costs, and new capital costs, such as a WMS implementation that quietly adds a couple of points to operating costs. At month-end, sales teams feel this directly because commissions are based on net profitability, and there's rarely a single dashboard that explains why the number came in where it did.
#### The Main Pain Points
Three problems sit at the top of the impact list, and they show up differently depending on who you ask.
**1. SLA and delivery-metric failure.** This is how third-party logistics (3PLs) get graded, and it often carries direct financial penalties. A missed SLA gets attention instantly — across operations, the 3PL, the trucking company, and the C-suite — because it's tied straight to the P&L.
**2. Transportation spend.** Consolidating shipments is an issue many companies overlook. Six purchase orders are addressed to the same destination, yet the goods are shipped as six separate loads because the POs were processed individually rather than as a single consolidated shipment. This shows up especially in government-related spend, where ship-to-origin discipline is often missing entirely. According to a logistics [study](https://www.mdpi.com/2305-6290/7/1/18) conducted in 2023, fixing this one thing can save nearly 40% in transportation costs.
**3. Margin erosion.** This is the one that cuts across every player in the chain, and it looks different depending on where you sit. Companies eat costs to honor commitments they've already made to customers. Short-sighted inventory forecasting turns a local hiccup into a systemic problem further down the line.
#### Why Margin Erosion Is the Most Sensitive Topic
Of the three, margin erosion is the one keeping operators up at night right now, and the reasons are structural, not seasonal.
Fuel and input costs are up across the board, and tariffs are compounding the pressure. Most companies have already pre-committed prices and promises to customers, so when costs rise mid-cycle, they eat the difference just to make the shipment happen. That means margins are compressing in real time, with no easy way to pass it forward.
#### How to Fix It\: Real-Time Operational Intelligence
The circular supply chain isn't going away, and it shouldn't. ERP, TMS, and WMS each do something the others can't. The problem is that nothing watches the seams where the loop connects, in real time, as it's happening.
That's the gap Row64 closes. Row64 is a real-time operational intelligence solution that sits atop the systems already running the business, without replacing any of them. It pulls live signals from across that entire loop and gives operators a unified visual canvas where they can see what's actually happening, not what was true an hour or four hours ago.
That happens in three phases\:
**Detect** the gap the instant a system's view diverges from reality\: inventory counts that don't match, a shipment that's behind cadence, a margin number drifting from plan.
**Understand** why it's happening, using context that connects the systems, instead of forcing someone to reconcile three screens by hand.
**Act** with zero decision latency, while the decision still matters, i.e., before a discount gets promised against inventory that isn't there, or before a missed SLA becomes a penalty.
In today’s supply chains, the Detection Gap is the space between systems that were never built to communicate in real time. Row64 closes that gap, so the next decision gets made on what's true right
[See a Demo](pageid:2c9de01a-0b4d-425d-aaa3-d3ad8e520c4f) | [Contact Us](pageid:ef2b99e5-7211-49d5-b85d-180b8cfd0060)
### What Is A GPU Spreadsheet? A Complete Guide
Often Row64 we get asked about many of the terms and technologies we are pioneering. After all, many are curious how exactly we deliver roughly [10x the speed of Microsoft Excel](/Gallery/ExProFiles.php) on personal computers with exact identical specs.
While the full answers to these questions are best found in [our whitepaper](/f/ytjfkt1s/row64-whitepaper.pdf), the short answer for questions surrounding our revolutionary 'GPU Spreadsheets' can be found in our comprehensive guide below.
1. [**What is A GPU Spreadsheet?**](#GPU_Spreadsheet)
2. [**What Are GPU Enabled Calculations?**](#GPU_Compute)
3. [**How Can I Do GPU Enabled Calculations?**](#How_Compute)
4. [**Speed Advantages of A GPU Spreadsheet**](#Speed_Advantages)
5. [**Examples of GPU Spreadsheet Software**](#Examples)
#### What Exactly Is A GPU Spreadsheet?
In simple terms a GPU spreadsheet is a spreadsheet whose software is 'GPU-enabled' to take advantage of the graphics processing unit (GPU) of a computer. This is in contrast to most spreadsheet software that use the computer's CPU for their calculations, as is standard for most applications. The main benefit of a GPU spreadsheet is improved speed and scale of calculations.
The reason why a GPU spreadsheet is so powerful is because a GPU is a processor very well suited for the complex numeric calculations required in spreadsheets. This is a function of the highly parallelized architecture of the GPU. Also known as parallel processing, this type of computing refers to a process by which similar calculations are performed across one data stream at the same time, (i.e. in parallel). You can even see this physically in the architecture of a GPU which has many more cores than a CPU all lined up in parallel rows.
The nature of parallel processing makes GPUs very fast at very specialized tasks. This includes graphics rendering, but also the types of formulas and display required for spreadsheets and corresponding data visualizations—hence the invention of the 'GPU spreadsheet'.
#### What are 'GPU-Enabled Calculations'? Is It The Same Thing As GPU Compute?
When you hear about GPU spreadsheets, you'll often also hear about "GPU calculations", often referred to as "GPU compute". To break it down, GPU-enabled calculations or GPU compute are non-graphics calculations that are either wholly or partially routed to a computer's GPU by software which has coded directly to the GPU, allowing it to understand and process the necessary data.
To better understand the nature of GPU compute, it's useful to remember that historically GPUs were just designed for graphics processing; it's literally in the name: graphics processing unit. Rendering complex pixel maps in real time for video games or video software was exactly the type of task for which highly parallelized processing was fantastic at. Through parallel processing, GPUs would "load" up one set of instructions enabling them to perform roughly [60-200 calculations a second](https://computer.howstuffworks.com/graphics-card.htm) from the same set of coordinate "instructions", thus displaying thousands of pixels in a cohesive, real-time 3D environment.
In recent years, as data sets have grown astronomically larger, data scientists and other businesses have increasingly looked to harness the computational strength of GPUs not just for graphics rendering, but for calculations as well. Uses of GPU compute have included data science, bitcoin mining, genomic sequencing, options market pricing, machine deep learning and climate modeling.
Putting the two concepts together, you'll see that GPU spreadsheets are applying GPU compute in the context of a spreadsheet user interface.
#### How Can I Do GPU Compute?
The unfortunate reality is that enabling your GPU isn't as simple as flipping a button. In order for any function to be allocated to the GPU it needs to actually be coded in its own language, which is optimized to the highly parallelized nature of GPUs. This is because CPUs and GPUs are so different in architecture that it isn't possible to create language that speaks to both.
In addition, GPU languages aren't (at least at the moment) commonly known. They're also somewhat time consuming as they were historically designed for graphics and thus you had to sort of "trick" your program graphics processor into thinking of data like graphics. To make matters worse, both Nvidia and AMD, the two main graphics processor manufacturers, have their own proprietary GPU language—CUDA and StreamSDK/Brook+ respectively. Apple has also introduced Apple Metal as a GPU language as they've begun adding their own GPUs into their products.
There are some attempts at using open source language such as [OpenCL](http://opencl) to 'hack' Excel into doing calculations through the GPU. However while these do show substantial speed improvements, they definitely don't have the robustness or user friendly design more analysts have come to expect from their software.
Ultimately for GPU-enabled calculations, one would need to have specific knowledge of GPU programming. This is challenging for even highly skilled programmers. There exists third party software that enables some translation from traditional programs to executable GPU files that do the computing, however these need to be written manually for each function.
At [Row64](/) we take pride in being one of the only companies out there making user friendly GPU spreadsheets that give everyday analysts the power of GPU calculations without needing to learn to code directly themselves.
#### Speed Advantages of GPU Spreadsheets
In general GPU enabled calculations are estimated to be in the realm of 10-100x faster than CPU calculations for similar tasks. However these speeds differ based on the task being assigned, as GPU calculations are not just used for spreadsheets but often for machine learning.
A couple speed benchmarks of the GPU calculations:
- A [test using a benchmark](https://towardsdatascience.com/parallel-computing-upgrade-your-data-science-with-a-gpu-bba1cc007c24#:~:text=GPUs%20render%20images%20more%20quickly,including%20the%20GPU(s).){target="_blank" data-id="05ab98df"} CIFAR-10 Object Recognition Model (a popular deep learning test for recognizing visual patterns) showed a 27x speed increase when using the GPU rather than simply the CPU.
- McKinsey & Co reported that [cycle times for machine learning can be 50 times faster](https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/tech-forward/the-business-case-for-using-gpus-to-accelerate-analytics-processing) when using GPU acceleration.
- When using identical computers, Row64 was able to get [17x faster sorting functionality and 91x faster](/Gallery/ExProFiles.php) our "hybrid engine", which optimizes both CPU and GPU processing.
#### Examples of GPU Spreadsheet Software
Despite the practicality of using GPUs for spreadsheets, the reality is that virtually all examples to date have been "hacks" from people using GPU language to port Excel data through external code. This has primarily been done through OpenCL, which stands for Open Code Language and it represents the first and mode widely used open source GPU language.
The team at [Stream High Performance Computing](https://streamhpc.com/blog/2016-09-19/accelerating-excel-opencl/) put together an Excel to OpenCL integration that essentially ports Excel Data to an external DLL (which is executable machine code file), thus outsourcing the calculations to the GPU. While an impressive feat, it's far from perfect as it still doesn't work with Nvidia's processors, and by the team's own admission, has precision problems between different sets of integers.
A team at [Prog.World](https://prog.world/we-connect-to-excel-gpu-and-speed-up-excel-300-times/) did a similar hack, using OpenCL and a DLL file to achieve very high speed improvements with their Excel VBA functions. While very impressive on a technical level, this project is one that requires a high degree of coding experience to execute, as it actually requires the user to code the entire process.
To fill this void between powerful GPU computing and user-friendly, intuitive software, we at [Row64](/) wrote our software from the ground up to take advantage of both the CPU and GPU. Our "hybrid engine" not only blows calculation and display speeds out of the water compared to everything else, but we also do so with an incredibly familiar and easy to use spreadsheet interface. No needing to learn how to code to the GPU yourself.
#### What Is A Python Spreadsheet? Is It The Same As A GPU Spreadsheet?
A Python spreadsheet is any spreadsheet for which the data manipulation is executed with Python script. A Python spreadsheet is different from a GPU spreadsheet in that while a GPU spreadsheet refers to the hardware enabled calculations a spreadsheet uses, a Python spreadsheet refers to the use of Python code in the software itself. Because they refer to different aspects, it's possible for a spreadsheet to be both a Python and GPU spreadsheet (such as is the case with Row64). This can give the full advantage of speed, scale and flexibility.
Because of its flexibility and efficiency working with large datasets, Python has increasingly been making its way into the world of data science and spreadsheets. A chart of Stack Overflow shows Python to be the fastest growing data science language by a large margin. Some current options like [openpyxl](https://realpython.com/openpyxl-excel-spreadsheets-python/) allow you to run python script directly into Microsoft Excel. Others, like open source [pyspread](https://pyspread.gitlab.io/) are natively written in Python entirely.
Our own Row64 takes spreadsheet-style formulas and expresses them as Python code, which a user can then save, modify or even share with other users. This flexibility means entire workflows or custom functions can be programmed in Python, and then exported and loaded into any other user's spreadsheet, without needing to replicate code. To make matters even simpler, we've coded over 250 of these Python-coded "data science recipes" for users to load in one click, to do some of the most commonly asked functions like time series forecasting or bar chart visualization.
### From Reactive to Real-Time: How Operational Intelligence Is Transforming Warehouse Operations
Warehouse operations are under constant pressure. Orders move faster, customer expectations are tighter, and even small delays can ripple into missed SLAs, rising costs, and strained teams.
Consider this\: According to [Gartner](https://www.gartner.com/en/articles/supply-chain-analytics), 76% of supply chain executives reported that companies are facing more frequent supply chain disruptions. Research from [McKinsey & Company](https://www.mckinsey.com/capabilities/operations/our-insights/risk-resilience-and-rebalancing-in-global-value-chains) shows that supply chain disruptions are common and that limited visibility slows response times.
The impact is immediate\: according to [Baymard Institute](https://baymard.com/lists/cart-abandonment-rate) and industry surveys, delivery issues are a leading cause of cart abandonment and reduced repeat purchases.
Yet many companies still manage their warehouses with a fundamentally backward-looking approach.
##### The Problem\: The Detection Gap
Most warehouse operations rely on retrospective reporting, such as shift summaries and performance dashboards that tell supervisors what has already happened. By the time a bottleneck is identified, the damage is being felt on the floor\: SLAs are slipping, requiring overtime or sudden shifts in personnel. In other words, warehouse management is happening reactively. That latency – the time between when a critical event enters the data pipeline and when a human operator can detect, interpret, and act on it – is called the Detection Gap.
Root cause is an outdated operational data pipeline that wasn’t built for the scale, speed, or complexity of modern warehouse environments. It lacks real-time visibility into the full operational flow, from inbound to fulfillment to dispatch.
##### The Shift\: Real-Time Operational Intelligence
Row64 changes this paradigm. Instead of relying on historical reports, Row64 introduces a real-time architecture and visual layer that acts on data at operational speed, with analytical processing applied in real time as events arrive and with full operational context — process structure, spatial awareness, entity history, and reference documentation — immediately present and interactively explorable as events unfold.
Unlike BI dashboards, this provides companies with\:
- Real-time visibility into operations
- Immediate alerts on emerging issues
- Data-backed recommendations before failures occur
Critically, Row64 is designed to support human-in-the-loop decision-making, so supervisors can act immediately, in live environments, and to complement the tools that warehouse operations teams already rely on — WMS platforms, BI tools, and data warehouses — all of which remain in place. No rip-and-replace. No workflow disruption.
##### The Warehouse Command Center\: A Live View of Operations
Imagine a warehouse command center that mirrors your actual floor. With Row64, that’s possible.
Row64’s GPU dashboards allow companies to overlay telemetry onto custom operational diagrams, so supervisors can see where those numbers are happening and how they relate spatially, all live and native in the browser. So, for example, supervisors can see\:
- A digital floor plan of the fulfillment center
- Active operational zones (e.g., picking, packing, staging)
- Real-time metrics like utilization, throughput, and headcount
Each zone can be continuously scored and analyzed. Patterns that signal risk, like rising queue depth combined with falling throughput, are detected as they emerge, not after the fact.
With a single click, supervisors can drill into any zone to view\:
- Live KPIs
- Real-time video feeds
- Current operational status
This creates a unified operational canvas of the floor in a single screen – something traditional BI dashboards simply cannot provide.
##### From Alerts to Action to Optimization\: Preventing Bottlenecks Before They Happen
Most systems stop at alerting you that something is wrong. Row64 can go further.
Using its API layer, warehouse teams can integrate custom predictive models that run directly on live data streams. These models don’t just detect issues. They can anticipate them.
The critical distinction is that these models are running on live telemetry, not on last shift’s batch export. Most operational environments today are fed by data that is already minutes or hours old by the time the model sees it, which means even a well-trained model is answering the wrong question. Row64 connects your predictive models to the current operational state, so the intelligence they surface is as fresh as the conditions on the floor.
So, for example, instead of a generic alert, a supervisor might receive a recommendation such as “Move three workers and one AMR from Aisle 1–4 to Pick Zone B.”
This recommendation is backed by full operational context, such as what pattern is emerging, what will break if no action is taken, and what the downstream impact might be. But rather than autonomously reassigning resources, it keeps humans in control, providing clear, actionable guidance so supervisors can make fast, informed decisions. This means bottlenecks can be resolved before they impact operations, and before a disgruntled customer is lost.
Row64 doesn’t stop at real-time monitoring. Using Row64’s APIs, supervisors can integrate an LLM of choice to analyze completed shifts and generate forward-looking recommendations, such as adjusting staffing levels to prevent over- or understaffing, or optimizing future workflows. This creates a continuous improvement loop, where every shift informs the next.
##### Quantifiable Impact on Warehouse Operations
For warehouse teams, the benefits are both immediate and measurable. In addition to capitalizing on ways to improve operational efficiency, Row64 can help reduce cost overruns caused by mistakes, keeping companies competitive. Some of the main areas of improvement include\:
- **Reduced SLA miss rates** through early detection and intervention
- **Lower overtime costs** by resolving issues before escalation
- **Faster response times**, reducing alert-to-action latency to seconds
##### Conclusion\: Eliminating the Detection Gap
Modern warehouse operations usually don’t fail because of a lack of data, but they can fail because insights from that data arrive too late.
Row64 closes that Detection Gap, helping turn live data into predictions, predictions into recommendations, and recommendations into one-click actions, all within a unified command-and-control surface. For operations where timing is everything and missed SLAs carry real cost, real-time operational intelligence isn’t a luxury, it’s a necessity.
[Schedule a demo](https://calendar.google.com/calendar/u/0/appointments/schedules/AcZssZ2RaGXr0NLZwjqzlvGvDdTpAx6FiNVZt6uVsfVDGfOYvncca53oGZg5saIPrmoGTrnZcEW5L48C) to see how Row64 could benefit your warehouse operations.
### Row64 V2.0 - Redefining Data Speed, Visualization and Collaboration
If our core philosophy of Row64 1.0 was to scale the data capability of the individual, our core philosophy of V2.0 is to scale the data capability of the team.
We accomplished this by not only further improving our core functions & stability of big data manipulation and data visualization—but by adding learning resources to immediately ramp up new users, and an incredible new data collaboration tool we're calling the 'Recipe Universe'. These features all converge to deliver a Row64 that's faster and easier to use than ever before.
Here's a breakdown of the exciting improvements based on your feedback that you can expect from our newly release V2.0:
##### Share Your Python With Anyone (Even Non-Coders)
One of Row64's most popular features is our '[data science' recipes](https://www.youtube.com/watch?v=58he0Cx8se8), which takes 300+ Python snippets, and expresses them as drag-and-drop spreadsheet formulas. These recipes package the power of Python into custom workflows at a scale and simplicity not possible in Excel, such as [Cohort Analysis](https://www.youtube.com/watch?v=uhGA6UFplfQ) and [Database Connectors](https://youtu.be/kNlYsPS0MAQ).
With Row64 V2.0, we pushed this concept further, opening up the ability for any Row64 user to create their own recipes by saving their Python scripts to the [Recipe Universe](https://www.youtube.com/watch?v=oSQ-JA7JsDY&list=PLzKFraMMbVENAkteAojsl7PyPL5IyVIRA). These neatly packaged 'no-code' solutions can then be immediately deployed across any team using Row64—including by non-coding analysts.
Plus, our Recipe Universe includes an IDE, meaning it streamlines not just the deployment but the entire creation process of Python recipes—including automated Python package and dependency management (full release notes at the bottom).
##### Large Scale Data Manipulation
As part of the Row64 V2.0 release, we increased the data frame manipulation capabilities by, allowing users to drag, edit, re-order and delete columns of up to 1 billion rows. This real-time ' [Slice & Dice](https://youtu.be/g96IKOyFmtM)' capability at this scale is truly groundbreaking, and cannot be found in any competing software.
##### Enhanced Visual Animation
Animation reveal features have become increasingly useful as a means to display time as a parameter within a visualization. Our V2.0 release utilizes our "easy animate" feature to enable users to animate our static data visualizations, to create [racing bar charts,](https://www.youtube.com/watch?v=rLgEuZ39GpM&list=PLzKFraMMbVEMJ32kJr7zOYPJ0xetexEET) [dynamically revealed line charts,](https://www.youtube.com/watch?v=rLgEuZ39GpM&list=PLzKFraMMbVEMJ32kJr7zOYPJ0xetexEET) and much more.
##### Learn By Example
In order to better serve our new users, our V2.0 release comes equipped with a [gallery of examples](https://www.youtube.com/watch?v=R_6l4VTxXq8) on our range of data visualization, manipulation and discovery tools. Browse example files of geospatial analysis, financial analysis, animated charting, text search to easily grasp the software's functionality.
#### Product Release Notes
##### Data Collaboration: Recipe Universe
- Recipes are JSON files that define:
- Spreadsheet style Formulas
- Functions (Python Defs)
- The Argument Mapping between Formula & Python including any pip library installations required
- How your recipe will work in the Row64 UI
- Formulas in custom recipes are very powerful, you have the power to modify the formula language in Row64 and offer a simple, one-click tool to less technical users
- Recipe Manager: IDE for development and deployment of python recipes
- Python Library Install (using pip):
- Libraries specified in the "Pip" field in .recipe files will be installed when the recipe is clicked on in the app
- All built-in recipes have been updated for automatic pip install
- New "Reset Libraries" feature
- If you corrupt your installation while experimenting with pip install, “Reset libraries” makes it easy to reset all libraries back to factory defaults
- Recipe Scanning:
- Recipes manage dependency trees of installation
- "At a Glance" display of recipe version and dependencies
- Cycle detection in poorly written libraries includes
- Start rescan: either from the UI Settings(->recipemanager->scan Pro version only) or restart the app
- Overhaul of Formula/Python Typing
- More predictable, changes in Python that are not argument mapped, now always explode
- Python into Formula Windows
- A more stable & predictable undo/redo system will now undo both views simultaneously.
- New Typing Features:
- End Key, PgUp, PgDn
- Default Location for Recipes
- C:\\Users\\\\Documents\\Row64\\\_Recipes\_V2\_0
- Share Recipes by dropping them into your recipe folder & rescanning
- [Download the detailed recipe creation documentation](/static/docs/RecipeUniverse_Doc.pdf)
##### Dataframe Manipulation: Slice & Dice
- Typing & editing directly in Dataframe cells
- Column reordering - 2 Methods
- Select the column(s) you want, then hover over the selected column header until you see the drag icon. Then click to drag and move column(s) to a new location
- Select the column(s) you want, then use ALT-Left or ALT-Right arrow keys to relocate
- Column reorder works with Undo/Redo
- Delete column
- Select the column(s) you want. Right-click on the column header to open the context menu. The last option is "Delete Columns."
- Note: If your dataset is >1M rows, there is no Undo for column delete.
##### Visual Analytics: Animated Charting
- Bar Chart reordering
- "Dynamic Reveal" style Line Chart
- Chart "grows" into place, the axes & ticks adjust with the data
- New keyframe system for X and Y tick spacing, Y axis upper/lower bounds
- X and Y tick keyframes blend is a fade-in/fade-out
- Y axis upper/lower bound keyframe blend is a linear interpolation of the values
- New "Edit Animation" button at the top right to enter the keyframe editing mode
- Click and drag to move keyframes around
- Shift-Click on the keyframe to edit the tick or axes bound values
- Use Arrow keys to move the keyframe around (hold down Shift for a bigger jump)
- DEL key to remove a selected keyframe
- "Auto" button at the top right to auto-reset the keyframes
- Pro Tip: open the Animation PPG timeline and scrub the animation so you can see the keyframes behavior
- Improved chart zooming & panning experience
- New radial charts recipes
##### Simplicity: Out of Box Experience
- New empty dataframe instruction box
- Explains dataframes, what they are for and how to get a spreadsheet
- Gallery template pop-up on app open
- Allows users to download project examples from our website
##### New Recipe Categories
- Cohort / Vintage analysis
- Database Connectors for ingest (includes on-prem & cloud DBs)
##### Misc Improvements
- Fixed the "Export CSV" button
- Updated the SettingsPPG UI
- Nvidia driver bug affecting the FilterUI and Dedup is now fixed for all driver versions
- Worksheet TEXTJOIN fixed
- Spreadsheet number format code fixes
- negative numbers
- Months with 31 days
- AVERAGEIF AND CODE
- Fixed Notebook Loading issues
- Installer improvements
- "on-demand" install of python libraries
- installs a small set of critical libraries
- Boot sequence improvements
- Recipe scanning is threaded
- Discovery templates are threaded
- Discovery templates have a new local image cache improving performance
### Introduction to Sentiment Analysis Using Product Review Data
Sentiment analysis using product review data is essential in understanding customer preferences and improving products and services. The rise of internet accessibility has led to an increase in unstructured data in the form of natural language, presenting challenges and opportunities for businesses seeking to extract meaningful insights from large volumes of digital information.
Here, we visualize a massive unstructured data set that we can zoom, pan, and select elements in real time to drill down into details within this cross-filtered set of either the positive things being said or, conversely, highly negative topics being discussed. This is a fantastic tool for understanding a company's online reputation and how customers and prospects speak of your company.
For example. in the above video, the sentiment is reasonably positive if we only consider support team input, but if we look at discussions on Reddit, it tends to focus on health and security concerns that are more negative. Now, we have the tools to drill in and understand discussions in that area. The massive amount of unstructured data can be manipulated in real time so we can zoom in and understand details much faster.
#### **The Mechanics of Sentiment Analysis in Product Reviews**
The core of sentiment analysis lies in categorizing the polarity of content into positive, neutral, or negative sentiments. This process involves sophisticated algorithms and techniques like Linear Regression, Naive Bayes, and Support Vector Machines (SVM). These enable efficient categorization of product reviews, allowing businesses to glean insights from customer feedback quickly. Identifying negation phrases in reviews is crucial in understanding true sentiment, as these phrases often include a mix of adjectives, adverbs, and verbs that significantly alter the sentiment conveyed.
The sentiment score computation is another vital aspect, where each word or phrase's sentiment score is calculated based on its occurrence in reviews with different star ratings. Tools like MonkeyLearn's Templates facilitate aspect-based sentiment analysis and keyword extraction, making this process accessible even without advanced coding skills.
#### **Harnessing the Power of Unstructured Data for Business Insights**
The value of unstructured data, such as product reviews, lies in its ability to provide deep insights into customer opinions and preferences. Sentiment analysis applications range from understanding customer needs, empowering market research, and competitive analysis to enabling quick adaptation to changing consumer preferences. It helps maintain consistent criteria in classifying sentiments, reducing subjectivity and allowing businesses to make informed strategic decisions.
In conclusion, sentiment analysis using product review data is a powerful tool for businesses, helping understand customer sentiment, improve products and services, and make informed strategic decisions. This approach is crucial for business success in today's digital world, where understanding and leveraging customer opinions can make a significant difference.
### Leveraging the Synergy of SQL, Python, and A1 Notation for High-Speed Data Analysis
*The following describes how Row64 dashboards combine Excel-style A1 notation with SQL databases to broaden the reach and power of dashboards for both technical and non-technical users.*
In data analysis and business intelligence, the fusion of databases, SQL (Structured Query Language), and A1 notation is revolutionizing the landscape. This convergence enables the creation of high-performance dashboards that provide real-time insights into vast datasets, fostering informed decision-making and driving business growth.
Imagine a scenario where a dashboard seamlessly integrates millions of sales and expense records, overlaying them with spreadsheets driven by complex formulas. At a glance, users can conduct high-level analyses on profitability, leveraging the interconnectedness of various tables, formulas, and filters. This holistic approach allows for deeper dives into the data, facilitating nuanced examinations of factors such as capital expenses versus labor costs.
One of the remarkable features of this integration is its ability to respond to dynamic inquiries swiftly. For instance, users can inquire about the impact on profitability if only the top five products were considered. Such queries are processed in real time, thanks to the efficient amalgamation of databases, A1 notation, and hardware acceleration.
Underneath the surface, the magic unfolds through the transformation of spreadsheets into sets of formulas, which are then compiled into evaluation graphs, similar to C++ and Java. These graphs, in turn, interface with database tables that leverage BYTESTREAM, a continuous-memory, high-performance format.
The result? *A dramatic improvement in speed,* with processes executing up to 95 times faster than traditional methods.
On the editing front, the marriage of spreadsheets and databases via A1 notation offers unparalleled convenience. Users can directly *manipulate and edit* data within the dashboard environment—bridging the gap between spreadsheet functionality and database capabilities.
This user-friendly interface streamlines workflow and encourages self-serve data exploration—empowering resource-limited departments to harness the power of big data through faster data manipulation and better visualizations.
We offer a mashup of powerful SQL querying and Excel-style A1 notation. We believe this could represent a paradigm shift in data analysis—ushering in an era of unprecedented efficiency and agility. As businesses navigate increasingly complex datasets, this integrated approach gives the power of seasoned data scientists to data analysts needing to access insights.
### The Enemies of Performance
#### Introduction\: The Challenge of Data Visualization
Computer hardware has evolved substantially over the past ten years. PCs, servers, and data centers are more powerful than ever and ready to tackle the demands of big data. As technology continues to decrease in cost, sensors and IoT devices proliferate, increasing global data ingestion. Data volume is soaring, but so is the available compute power. Although high-end hardware is more available than ever before, a major challenge businesses face today is processing their data.
While modern hardware is equipped to meet the increasing demands of big data, conventional dashboards are often unoptimized. Many visualization platforms experience bottlenecks and slowdowns under high pressure. Even products that advertise live analytics are rarely designed to take full advantage of modern hardware. Today’s hardware is more than capable of undertaking modern data challenges, but many dashboard solutions are falling behind. Businesses must be able to draw timely insights from their data, so delayed processing times are no longer acceptable. Code inefficiencies and legacy architectures are two obstacles that many dashboards are confronted with.
##### Era of Inefficiency
Modern software development tends to rely more on hardware improvements than performance coding. Software always benefits as hardware compute speed increases, even without explicit optimizations. But, this passive approach of depending only on hardware speeds misses opportunities to utilize resources. Even when backed by the power of modern hardware, inefficient code struggles to meet performance requirements in high-stress data environments, such as live data streaming, visualization, and analytics.
In the early days of computing, hardware was limited and costly. Software engineers had no choice but to optimize their code to make the best use of the available hardware. Consequently, high performance code was at the forefront of design. As hardware has become more powerful, however, performance coding has been less of a priority.
Modern hardware has allowed for a relaxed perspective towards execution speed. Although this degree of performance coding is no longer necessary in everyday applications, performance code is still relevant for heavy-duty use cases. When data applications just rely on hardware improvements for speed gains without considering the need for optimizations, they inevitably run into performance bottlenecks.
##### Legacy Database Code
Many popular data tools have longstanding origins and were developed for legacy hardware. Though these tools were cutting-edge in their day, hardware capabilities have changed. Today’s hardware has opportunities for newer techniques in memory utilization, storage access, and CPU and GPU compute, which were not available in the past. The difficulty in integrating new performance technologies into existing stacks is a major challenge for these older systems. In many cases, their codebases would have to be rewritten to achieve perfect optimization.
##### Row64’s Modern Performance
Row64 is a real-time visual business intelligence platform that is built from the ground up for maximum speed and scalability. Row64 employs modern performance principles and taps into the full potential of high-performance hardware. Performance is at the core of Row64’s design.
Row64’s founding team has a long history in game technology. Modern game engines prioritize performance and take advantage of chip advancements to achieve the high frame rates needed for AAA multiplayer games. These games are both computationally and graphically intensive, so performance is a priority. Not only do they involve complex computations, such as simulations and real-time rendering, but they also stream interactive data.
The data industry is yet to catch up with the best practices of the game industry. Row64’s expertise in game technology, graphics, and data analytics allows it to herald a new generation of dashboard, data streaming, display, and analytics tools. Row64’s custom stack is a game engine, but for data. Row64’s goal is to break speed records and raise the standard for data processing and visualization.
#### Identifying the Enemies of Performance
A primary strategy of high-performance coding is to avoid performance pitfalls. This involves identifying and effectively avoiding performance bottlenecks. Think of the classic game Pac-Man, where the player has to navigate a maze while avoiding the ghost enemies. Similarly, creating high-performance software involves navigating between all the parts of making a good product (hardware, software, user interface, and design), while avoiding costly processes or practices that reduce performance.
Row64 has identified four major enemies of performance in streaming and dashboard software\:
- Memory allocation
- Cache misses
- Ignoring hardware
- Garbage collection
##### Enemy #1\: Memory Allocation
**Memory Allocation Overview**
Programs need space in memory to run and store information. Memory allocation is the process of designating space in a system’s mass storage (SSD) or memory (RAM) for a program’s working data. When a program needs space, it must request a block of memory from the operating system. The operating system finds a suitable block and returns its address to the program, and from there, the program can use the space. When the program no longer needs the allocated memory, it releases it back to the general pool, making it available to other processes. Allocating and releasing memory is one of the major bottlenecks to performance.
**The Cost of Memory Allocation**
Memory allocation is slow. There is a time cost when a chunk of memory is requested, allocated, and when it is freed. This time cost varies and is influenced by\: whether it occurs in RAM or the SSD, the type of hardware, and the amount of memory being requested. Current memory allocation speed estimations range from under 100 μs (microseconds) to 400 μs per megabyte. If code repeatedly requests and releases memory, it incurs the time penalty each instance. It’s much faster to allocate once and reuse the memory, rather than releasing and requesting it again.
An area where memory allocation has an especially high cost is copying. Copying between memory blocks is expensive and introduces additional penalties. Every time a block of memory is copied to another block, the CPU not only needs time to allocate new space, but to transfer the bytes from the source to the target location. Performant code minimizes copies as much as possible. This strategy is referred to as the “Zero-Copy” technique. What this means is that, when you have the ability to work on an existing piece of memory, don’t make a copy.
No software truly eliminates all copies, however. This is especially true for web-based applications, since transferring information across networks requires copies. While good code design can’t truly eliminate all copies, it can greatly reduce them.
**Siloed Stacks**
Software often revolves around a technology stack. A stack-based architecture deploys discrete layers of applications that work together to form a comprehensive platform. Generally, data must be copied and transferred between the layers for the platform to work. Stack-based architectures result in a segregation, or siloing, of data.
An example of a stack-based platform is Apache Superset, which is an open-source dashboard solution. A Superset stack may include a MySQL database on the back end, Python and SQLAlchemy for compute, Flask for network communications, and React for browser display and the UI. Each component on the stack is separately developed by different teams, which typically don’t share the same data format. Each component of the stack is effectively in its own silo. Additionally, this example Superset stack involves at least three languages\: MySQL in C, Python, and React in JavaScript.
Stack-based platforms constantly copy and translate information across layers, just to complete simple tasks. In the Apache Superset example, a piece of data has a long journey before it arrives to the end user; data might need to be copied from the database to the computing service, be processed, have the results copied, and finally be pushed over the network to the front-end display.
Every time communication occurs between the layers, data must be transferred. Each stack component will need memory to be allocated, and then data will need to be copied and moved. If the components use different data formats, extra CPU resources are needed to parse and restructure the data. Language conversions between components involve a more severe cascade of memory allocations and copies.
**Python Case Study**
Python is a popular scripting language that is used extensively for data analytics and processing. Python is generally considered to be a user-friendly language that abstracts away the difficulties of lower-level languages, such as casting variable types and memory management. Python is built on C.
Under the hood, Python stores data in PyObject structures, which are dependency graphs of C structures. The PyObject structure includes a pointer to a second structure, called a PyTypeObject, that contains all the connections and details used to manage the object. Creating a single PyObject is expensive, as it not only requires memory allocation, but it additionally incurs a noncontiguous memory penalty, since, internally, Python works with linked lists of pointers.
The following experiment explores how a stack component, written in C, might pass a single date value to a Python application. In C, a single date value can be stored as an 8-byte structure that represents the number of nanoseconds from an epoch. We conducted a simple test to track where and how a single data is stored and processed when it is passed to Python. This was done by break-pointing Python’s internal code.
For this example, consider a date value\: 1/2/2022. Converting this 8-byte date to the equivalent date in Python would require newly allocating\: 16 + 408 = 424 bytes. This conversion is needed simply to hold the basic information. While a single value isn’t significant, how would performance be affected with 100 million dates in the dataset? Each of the 100 million dates would need to generate PyObjects. Not only is there the cost of simply copying the records, but there will be a substantial overhead for the additional memory allocations. Language conversions between siloed layers of a technology stack can introduce some of the most severe performance penalties.
Python plays an important role in many data analytics use cases. Within the Python community, tools like NumPy and Apache Arrow have attempted to reduce the PyObject penalty and implement performance improvements. However, the best solution is to avoid extra data copies, especially cascading copies. It is better to work in a unified system with vertically-integrated stack components. This leads to speed gains by avoiding copies, parsing, and penalties due to communication between components written in different languages.
##### Enemy #2\: Cache Misses
**The Memory Hierarchy**
Computer memory hardware is organized in a hierarchy based on the speed, cost, and capacity of the technology. Near the bottom of the hierarchy is mass storage, which includes technologies like HDD and SSD. Mass storage drives are non-volatile and have a high capacity for long-term storage. Although SSDs are substantially faster than HDDs, mass storage remains comparatively slower than other forms of memory. Mass storage technologies are also the least expensive on the hierarchy.
Above mass storage is RAM, which is a computer’s main form of working memory. While mass storage drives are for long-term data preservation, RAM is used to temporarily hold a program’s active data. The underlying technology for RAM is DRAM (Dynamic Random Access Memory). DRAM is volatile, which means that it does not preserve data when it loses power. DRAM is faster, but more expensive, than SSD technology.
Near the top of the hierarchy is CPU cache. Cache is a fast form of SRAM (Static Random Access Memory) that stores information that the CPU immediately needs to perform calculations. SRAM is faster than DRAM, but is substantially more expensive. A CPU commonly has three cache layers\: L1, L2, and L3. SRAM technology is extremely fast, but due to its high cost, it is deployed in small quantities.
While prices and speeds vary significantly according to the manufacturer and quality, the following table compares current storage technologies found in common, non-specialized consumer desktop systems\:
The further up the hierarchy, the faster and more expensive the storage technology becomes. But, due to the increasing price, the capacity decreases.
When a program runs on a computer, it is first copied from mass storage to RAM, and the program remains in RAM throughout its execution. As the program runs, relevant bytes of data are forwarded from RAM to cache for processing. Since cache is small, data must constantly cycle between RAM and cache. The idea of the memory hierarchy is to keep relevant bytes in faster forms of memory as much as possible to maintain high speeds.
When a program needs bytes of data, the system searches from the top of the memory hierarchy down. L1 cache is the first location the CPU draws data from. If the requested data is not in L1, the CPU checks L2, and then L3, and continues down the hierarchy until the requested data is found. Searching through the hierarchy is time consuming, especially when slower forms of memory are queued.
**Caching**
Caching is the process of moving frequently accessed data from main memory to cache to better serve future requests. This way, when the same data is requested again, it can be immediately processed from cache, saving the time of searching through slower RAM every time it’s needed.
The best scenario is for the needed data to always be available in cache. But, since CPU cache is small, bytes need to be constantly swapped between cache and RAM. Caching uses prefetching, which is a process that attempts to retrieve data the CPU might need next, before it asks for it.
Prefetching is based on the principles of temporal and spatial locality. Temporal locality is the concept that the CPU is likely to use the same data again soon after it initially accesses it. In other words, when the CPU needs information, temporal locality predicts that the CPU might need that same information again in the near future. So, it would be advantageous to temporarily leave it in cache. Spatial locality refers to the tendency for the CPU to request data that is physically near to the location that was recently accessed in memory. With spatial locality, the prefetcher grabs bytes of data that are around the requested data, in an effort to preload cache with relevant data.
**Cache Misses**
When the needed data is present in cache, the CPU is able to quickly perform the needed calculations and proceed to the next step. When the data is not in cache, however, the system must locate and transfer the data to cache before the CPU can continue. A cache miss occurs when the CPU requests data that is not present in cache. In this situation, the data request propagates down the memory hierarchy until the data is found. The CPU first searches the L1 cache. If it is not there, it proceeds to L2, and then L3. If the requested data is not present at all in cache, it will continue down to RAM. When the requested data is found, the data is transferred to cache and the processing resumes.
Each step down the hierarchy is slower than the previous step. L2 cache is slower than L1 because it is larger and further from the CPU core. L3 is slower than L2 for similar reasons. RAM is significantly slower than L3. For high-performance software, cache misses are costly; each step down the memory hierarchy is nearly an order of magnitude slower than the previous step. Cache misses can potentially take hundreds of clock cycles to resolve.
**Caching Benefits of Contiguous Memory**
Since cache is the fastest form of memory and closest to the CPU, it is ideal for relevant data to be present in cache as often as possible. Cache misses are extremely costly, so organizing data to optimize prefetching is crucial. Prefetching is heavily influenced by the underlying organization of the data in RAM.
When a program’s data is adjacent, it is easier for the prefetcher to retrieve the requested bytes along with the next block. This is the principle of spatial locality. When data is fragmented across memory, spatial locality is no longer effective, resulting in more cache misses and slowdowns. Since relevant data needs to constantly be fed into cache, contiguous memory is crucial.
##### Enemy #3\: Ignoring Hardware
**Missing Hardware Opportunities**
Hardware has a lot more to offer today than in the past, but a lot of software seldomly takes full advantage of the available resources. Some of the commonly missed performance-boosting opportunities include parallelization, using the GPU, and caching optimizations.
One reason many software applications aren’t fully optimized for current hardware is because they were developed for older hardware and the implementation was simply preserved over time. For example, GPUs were exclusively used for graphics through the late 90s, but in recent years they’ve become useful for computing. Applications such as crypto mining and machine learning benefit from GPUs. In the past, moving data between the CPU and the GPU was slow, and gains from the GPU were lost in the transfer time. With the introduction of PCIe 5.0, however, this bottleneck has been significantly reduced.
**Legacy Code Challenges**
Legacy applications face a formidable challenge with modernizing their code bases. An application’s core architecture defines how much performance can be achieved. Legacy code can hinder the ability to incorporate new performance features into the existing code base. Some notable performance opportunities in recent years include innovations in\: parallelization, integrated GPUs, and GPU access for browser applications. Advances in these areas can’t be integrated into many legacy applications without reworking the entire core architecture; in other words, a rewrite.
**Parallelization**
Most computers today have at least six cores, with each core supporting two logical processors. The cost of adding cores to a computer has decreased over time, and the speed and capacity of each core has increased exponentially. Historic data tools were written and optimized for single-core performance. Not only that, but many scripting languages don’t use parallelization at all.
Parallelization throughput has also increased enormously on GPUs. This parallelization is the most visible in video games, where complex scenes can now be rendered at hundreds of frames per second with today’s leading GPUs. Legacy applications that were optimized for single-core performance would not be able to take advantage of modern parallelization without being rewritten.
**Integrated GPUs and GPU on the Browser**
GPUs were not widely available on consumer hardware in the past, so historic data applications could not rely on them for performance gains. Today, integrated GPUs are available on almost all computers, tablets, and phones. In addition, integrated GPU speeds have increased enormously in the last five years, approaching the performance of discrete GPUs.
Within the last ten years, browsers started to embrace high performance technologies, including\: WebAssembly, WebGL, and WebGPU. WebGL, for example, allows web applications to access a system’s GPU, but did not reach 95% adoption until around 2023. Browser applications built prior to this relied on HTML and JavaScript for display and compute.
Most leading dashboards were built for legacy technology. Row64 stands out because it was built for modern hardware and browsers, with the GPU in mind.
##### Enemy #4\: Garbage Collection
**Overview of Garbage Collection**
Garbage collection is the process of reclaiming memory that is no longer needed by a program. Higher-level programming languages typically have automatic garbage collection, whereas lower-level languages, like C, C++, and Rust, allow for more granular memory management. Although automatic garbage collection is convenient and simplifies code, it often comes with the cost of reduced performance.
**Performance Penalties**
For languages that support automatic garbage collection, the garbage collector runs periodically without intervention. When the garbage collector initiates, it first searches for unreferenced memory. This search consumes clock cycles, and can sometimes go as far as to cause a brief hiccup or slowdown in the program during execution. For high-volume applications that require live updates, these pauses can be detrimental.
Garbage collection can also contribute to memory fragmentation. As portions of memory are allocated and deallocated over time, memory becomes scattered and nonadjacent, which could lead to performance degradation.
#### Combating the Enemies of Performance
##### Unified Memory
Unified Memory is the core strategy of Row64. Draw and compute across all components of the Row64 platform are based on the principles of Unified Memory. With Unified Memory, data is\:
- **Vertically integrated\:** Making communication across the platform seamless.
- **Centralized\:** Reducing the need for copies and unnecessary memory allocation.
- **Contiguous\:** Taking advantage of cache prefetching.
- **Hardware-driven\:** Making the most of hardware when it is available.
**Vertically-Integrated Stack**
Row64 is a unified stack where components are vertically integrated and optimized for working together. Components like the front-end display and back-end compute service share the same memory layout. The data format is also consistent between all Row64 processes, so there is no data transformation or parsing between parts. This extends to components running on different hardware; communications between the Row64 Server and Dashboard Client likewise use the same format. All components are able to read the bytes of any data communication and retrieve the necessary details on demand without extra copies.
Row64 deploys its own RAM database, WebSocket server, and CPU/GPU parallelized compute engine, within a single Linux service. Because these run as a single process, they share RAM. That means Row64’s internal database, computing service, and network I/O can have immediate access to the same memory, eliminating the need for extra memory allocations, copies, and data transfers.
**ByteStream and RamDB**
Modern dashboards must be able to handle massive datasets and perform interactive investigations at fast speeds. For instance, cross-filtering allows users to drill in from a large dataset to a fine subset of details. This is valuable for tracking anomalies, discovering hidden data patterns, and transforming raw data into actionable insights. Cross-filtering while streaming data updates is especially demanding.
A primary objective for Row64 when developing the byte layout for the data formats used across the platform was to account for both scalability and interactivity. The byte layout is optimized for tasks on both the SSD and RAM.
The primary data format used by Row64 is ByteStream. ByteStream is optimized for contiguous memory, which minimizes cache misses. ByteStream is a binary dictionary, where data is stored as key-value pairs, but it is organized contiguously to yield high performance prefetching results.
Row64 also contains RamDB, a RAM database designed for high-speed data access and manipulation. RamDB uses the ByteStream layout, but includes additional specifications for storing tabular data records. RamDB’s layout is contiguous, so operations in memory reap the benefits of prefetching. Since tabular data also needs to persist when not in use, the RamDB design also considers the need for performance when reading from and writing to SSD.
RamDB’s byte layout takes advantage of modern SSDs and byte-level indexing for short and fast random access reads. This allows Row64 to extract and manipulate targeted chunks of data instantly. This is particularly useful for cross-filtering drill-ins, as records can be quickly accessed and used for compute operations.
Prior to 2010, storage technology was primarily hard disk drives (HDD). HDDs access data from a magnetic, rotating platter, so the fastest way to access data was to read it sequentially in one block. Fragmented or non-sequential data reads on HDDs incurred a heavy performance loss. The introduction of SSDs, however, meant that data could be read anywhere without the penalty. While HDDs may seem like a thing of the past, most software stacks still use legacy code that’s optimized for long reads.
Row64 set out to design a new display and compute stack for dashboards that would break records. The goal was for the dashboard to excel at cross-filtering and data drill-ins, but to also be responsive and easy to use. Row64 views cross-filtering as a frontier that drives innovation.
**Hardware-Driven Compute**
For the best performance during compute-heavy workloads in graphics and data evaluation, Row64 is designed to adapt to the underlying hardware. This adaptability is achieved through the Hybrid Engine. When multiple hardware resources are available, Row64’s Hybrid Engine adjusts to use the optimal resources. Row64 also works at a low level, which enables it to have a direct connection with the hardware.
Additionally, Row64 is highly threaded and parallelized on both the CPU and the GPU. Row64 can utilize all available cores on a CPU, if configured to do so. Network communications and requests, along with dashboard requests and evaluations, are also parallelized.
#### A New Generation of Data Visualization
Data processing and visualization platforms are presented with a quartet of challenges\:
- Large data scales
- Need for high speeds and interactivity
- Multi-tenancy
- Live streaming updates
While existing data visualization platforms have scrambled to keep up with these challenges, Row64 was built to address them. Row64 was designed to work closely with hardware at a byte level, and in some places, at a bit level. Row64 bypasses many of the performance pitfalls by avoiding high-level stack components, and is able to push the speed barriers of real-time visualization.
In a fast-changing industry where the demand for high-speed data visualization and streaming updates are growing, Row64 is well positioned to elevate the standards for a new generation of dashboards and data visualization.
### Maximizing Retail Performance with Real-Time Operational Intelligence
In today’s fast-moving retail environment, the ability to understand and immediately act on data has become an important competitive differentiator. Retailers collect millions—sometimes billions—of transactional and behavioral data points every day, yet most organizations still struggle to turn that information into timely, actionable insight.
Traditional BI dashboards and reporting tools weren’t built for the velocity or complexity of modern retail. They can summarize historical information, but fail to capture what’s happening now. For retailers seeking to improve sales performance, optimize store operations, and react to consumer behavior in real time, this delay can result in lost revenue.
#### The retail data problem: fast signals, slow decisions
Retailers are awash in data—SKU-level transactions, point-of-sale activity, inventory movements, promotional responses, shopper traffic, and digital interactions. But even with all this data, teams still face:
##### 1\. Operational lag
Insights arrive after opportunities have already passed. By the time a missed promotion, out-of-stock condition, or emerging trend is discovered, the window to act has closed. The brands that can turn massive datasets into live operational awareness will get ahead.
##### 2\. Fragmented dashboards and incomplete views
Merchandisers, store operations teams, and analysts navigate multiple disconnected tools that provide summaries of historical data, creating a fragmented, delayed picture of what's actually happening in the business.
##### 3\. BI tools built for yesterday’s data
Legacy systems can handle batched data updates and static reports, but today’s data environments are different. Data can be streamed. It can be 2D or 3D. Retailers must be able to see it all, and in real time, to get a full understanding of their operations.
##### Row64 brings real-time operational intelligence to retail
Row64 is the operational intelligence hub for data-driven enterprises, including retail. For retailers, Row64 transforms massive datasets, streaming, and real-time data into live retail analytics, enabling better decisions across merchandising, operations, pricing, inventory, and store execution.
Unlike legacy BI solutions, Row64 isn’t simply “faster dashboards.” It represents a new architecture for perception, leveraging CPU, GPU, and in-memory processing to deliver:
- Real-time SKU-level analytics
- High-precision GPU-rendered interactive visualizations
- Live operational intelligence across billions of data points
- High-level summaries to record level details directly in the browser
Where traditional BI analyzes the past, Row64 illuminates what’s happening in retail environments now.
##### Real-time analytics that drive retail optimization
Row64 brings together every stream of retail data—transactions, IoT sensors, inventory data, POS activity, foot traffic, digital interactions—into a single, continuously updated intelligence hub.
##### Real-time SKU-level intelligence
Retailers can track SKU performance in real time, uncovering emerging demand patterns, underperforming items, pricing issues, promotion lift or drag, and inventory risks and anomalies.
##### Interactive in-store analytics and floor layouts
Retail analytics is not just about pricing and SKU movement. It’s also spatial. Row64 connects sales and inventory data with dynamic store floor plans, enabling teams to visualize performance by location, view in-store product placement relative to sales, evaluate planogram effectiveness, and improve traffic flow.
This creates a live, spatial understanding of store health and performance.
##### Prescriptive recommendations for retail teams
Row64 doesn’t just present current data—it enables retail teams to run predictive models based on sales, product placement, and more. That, in turn, allows stores to adjust pricing more quickly, modify promotions, improve shelving strategies, and respond to emerging trends.
In other words, Row64 enables retail teams to become more proactive.
##### Bottom line: “Real-time” is retail’s new competitive advantage
In 2021, the retailer Target posted profits of $6.9 billion. Just a 1% increase could add an extra $69 million to the bottom line. Seeing, deciding, and acting faster than their competitors through real-time data could be the difference.
With Row64’s delivery of operational intelligence in real-time, organizations enjoy faster decision-making, better promotional and pricing performance, and the ability to plan rather than react to the past. And that means better store performance – and more profit – for the entire organization.
Check out the [live demo](https://row64.com/products/demos/?demo=retail-price-optimization).
### The Internet’s Best Data Visualizations 2022
The old adage goes "Seeing is believing". However when it comes to data visualization, we believe the truth is that seeing is understanding.
Ever since the world's first data visualization was credited to Flemish astronomer [Michael Van Langren in 1644](https://www.dundas.com/resources/blogs/introduction-to-business-intelligence/brief-history-data-visualization#:~:text=In%201644%2C%20Michael%20Florent%20Van,visual%20representation%20of%20statistical%20data.), data visualizations have been used to make the understanding of data easier, faster, and more intuitive. After all, while numbers allow for complex calculations, human minds are evolutionarily designed to have strong visual processing ability, meaning we are able to understand data visualization about 60,000 [times faster than we do just text.](https://carlsonschool.umn.edu/faculty-research/mis-research-center)
However a lot has changed since the 17th century and now the variety and detail of data visualizations have exploded inline with the amount of data itself. As many aspiring data scientists look to the frontier of data visualizations to see how they can showcase their findings, we thought it beneficial to highlight our favorite data visualizations to date.
To make our list easy to digest, we highlight each posts' type of the data visualization, as well as the source and link to the original project. We pored through roughly 300 data visualizations to find the most unique and effective of each type of data visualization, with an emphasis on highlighting a diversity of data visualization styles.
We hope you enjoy this list as much as we enjoyed putting it together.
#### #15 "Cover Mania"
##### Source: [Michel Mauri](https://www.behance.net/gallery/7654745/Cover-Mania/modules/58313289)
##### Type: Sankey Chart/Timeline
As any avid audiophile will tell you, debate is the foundation of any good music appreciation. Wanting to determine what legendary musicians were having the most ripple effect in music, Italian data visualizer Michele Mauri created a spiral graph that shows which artists were having their music catalog covered the most by others. The result is a clear and beautiful illustration that uses color coding and line density to quickly demonstrate whose covers were dominant each year, and how that changed over time. An added bonus are little nodes for select years that specify which song was the most popular cover that year.
#### #14 "Rise of Partisanship in the US"
##### Source: [Mauro Martino](https://www.mamartino.com/projects/rise_of_partisanship/)
##### Type: Scatter Plot
It's no secret to anyone that partisanship feels higher these days—but is it really? Data visualizer Mauro Martino attempts to address this question by outlining U.S. House of Representative Members as blue and red nodes, and using a linear-repulsion model with Barnes Hut optimization to draw connections for when Representatives agreed with those across the aisle. We're huge fans of scatter plots like this as the viewer is able to see trends over a 60 year period virtually immediately (whether or not they like the results).
#### #13 "The Dude Map"
##### Source:[Quartz](https://qz.com/316906/the-dude-map-how-american-men-refer-to-their-bros/)
##### Type: Interactive Heat/Cloropleth Map
Hey dude, do you call your bro "pal"—or "buddy"? This fun and interactive heat map from Quartz uses geo-tagged tweets on a county level to explore the man-on-man vernacular across the United States. Forensic linguist Jack Grieve used hot-spot testing (a common technique in spatial analysis) to understand geographic trends by measuring concentration of dude-based language compared to the frequency of that same language in surrounding areas. The result is a fun and simple heat map that lets you know if your duder-onomy is similar to those around you.
#### #12 Income and Financial Stability
##### Source: [FiveThirtytwenty](http://fiftythirtytwenty.com/)
##### Type: Stacked Bar Chart
FiveThirtyTwenty was founded on one concept: budgeting shouldn't be complicated. By spending 50% of income on needs, 30% on wants and saving 20%, Americans should be able to achieve some degree of financial stability. The unfortunate reality? The average American is spending way more on needs (71%), and way less in savings (12%). While some data visualizations are impressive because of their complexity, this one is impressive for its simplicity—showing readers exactly how we miss the bar.
#### #11 Social Network Graph Evolution
##### Source: [Evandro Damião Barbosa](https://vimeo.com/285589467)
##### Type: Animated Chord Diagram
What happens if you map 1000 Facebook friends as nodes in a 3D space? That's what animator Evandro Barbosa sought to examine as he explored the more than 33,000 connections his sample cluster has amassed, with varying degrees of interconnectivity and isolation. While mesmerizing, our only critique on this is there is very little written about the methodology, making it more art than science.
#### #10 "Film dialogue by Gender and Character"
##### Source: [The Pudding](https://pudding.cool/2017/03/film-dialogue/)
##### Type: Interactive Bar Chart
The team at digital agency Pudding wanted to answer one question: how many movies are actually about men? Putting on their data science caps, they parsed through 2,000 screenplays (the largest undertaking of its kind), found characters who spoke at least 100 words, and mapped that to the gender of their character on IMDB. The result is an interactive sortable bar chart with the most complete breakdown of which films are male dominant, female dominant and have gender parity.
#### #9 "Nobels, no degrees"
##### Source: Accurat (now hosted on [Behance](https://www.behance.net/gallery/14159439/Nobel-no-degrees?locale=en_US))
##### Type: Combination Line/ Bar/Sankey Graph
This absolute combo of a graph uses three different types of graphs to provide the most comprehensive look into who Nobel laureates are. The six line graphs are color coded and broken up according to the 6 categories of Nobel prizes, and the nodes on each graph represent the age of that year's winner, with a timeline running left to right, and a bar representing the average age for the cohort winners for that category. Nodes with women winners are circled.
In addition to the line chart for age, the graph has a bar chart showing level of education attainment, a Sankey chart for showing what University the laureate went to, and a stacked bar chart at the bottom for frequent hometowns of the laureate winners, in 30 year increments.
#### #8 "A Month of Breaking News Visualized "
##### Source: Washington Post
##### Type: [Dot Matrix Timeline](https://www.washingtonpost.com/news/politics/wp/2017/05/18/a-month-of-breaking-news-alerts-visualized/)
The folks at Washington Post have had a sneaking suspicion: "breaking" news seems to be happening a lot more frequently these days than ever before. Are the world's catastrophe's truly accelerating amidst a globalized setting, or are news agencies simply taking advantage of your attention span. To answer this question they mapped breaking news by source and day, to track which news outlets were "breaking" news the most relative to their peers.
#### #7 "Volume Jump Shooters"
##### Source: [Kirk Goldsberry](https://twitter.com/kirkgoldsberry/status/1490413147160821765/photo/1)
##### Type: Bubble Plot
Kirk Goldsberry has been making a name for himself by bringing the NBA's most interesting stats to life. His efficiency scatter plots are some of his most liked, using color, direction and size to immediately convey a multitude of data points. In the graph below, fans can quickly see that not only is Steph Curry one of the most high efficiency shooters in the league, but he does so with tremendously high shot volume. Russell Westbrook, often criticized for being the opposite, shows up as the inverse to Curry's efficiency, while still maintaining the same high shot volume.
#### #6 "The Pandemic In The US In 60 Seconds"
##### Source: Reddit - [r/DataIsBeautiful](https://www.reddit.com/r/dataisbeautiful/comments/q4dc8b/oc_the_pandemic_in_the_us_in_60_seconds/)
##### Type: Animated Heat Map
While the pandemic may be a tired subject at this point, this animated heat map is certainly a sight to behold for data enthusiasts. What makes this visualization so compelling is that it exemplifies how animations can perfectly encapsulate the dimension of time in a previously static data visualization. Because the pandemic was always about viral spread, the ability to show just how quickly a previously uninfected area can go to dense viral loads and cascade to neighboring communities is an inseparable part of this graphic. As proponents of adding animated dimensions to our graphics, we found ourselves big fans of this graphic, despite the morbid news.
#### #5 "The Decline of CA's Water Resources"
##### Source: Row64
##### Type: [Animated Line/Bar Chart](../DataViz/)
Much has been made of Lake Mead's declining water resources, and so it was of no surprise to us when our animated line and bar chart became one of the top posts on reddit's r/dataisbeautiful. By taking a 30-year retrospective on California's 10 largest reservoirs, we are able to contextualize the current drought to cyclical ones in the state's past — with alarming results.
#### #4 "Web Browsers For The Last 28 Years"
##### Source: [James Eagle](https://www.youtube.com/watch?v=H52DmvfzDWM)
##### Type: Racing Donut Chart
When James Eagle released this visual it quickly became one of the most shared data visualizations of the year. Once again demonstrating the power of time as a dimension in data viz, this is an extremely fun watch for anyone who lived through the browser shift of the 2000's, beginning with the collapse of the dominant Netscape Navigator, and the subsequent takeover by Mozilla, then Safari and Chrome. Telling what is ultimately a 30-year business story in 3 minutes, this visualization shows that data is both easy to understand and interesting to watch when presented correctly.
#### #3 "When Each Social Media Platform Was Generating Its Peak Buzz on Google"
##### Source: [Chartr](https://read.chartr.co/)
##### Type: Line Chart Grid
A counterexample to animated charts, this graphic by Chartr uses a 3x3 grid to compare 9 different social media platforms over the same period to show their respective periods of popularity. A reason for the effectiveness of this method is two fold: firstly, stacking 9 companies on one graph would likely get so crowded as to make seeing any of the individual components too difficult. Secondly, because the Y axes of each graph isn't uniform, comparing them on one chart would conflate absolute magnitude with respective popularity. Segmenting out each graph individually allows for the best ability to see each individual "spike".
#### #2 African Riverways
##### Source: [@PythonMaps](https://www.instagram.com/p/CZqzWhlsBZD/?hl=en)
##### Type: 2D Python Map
It's no secret that here at Row64, we're fans of both Python and data visualization. So when we came across this Instagram account creating visualizations only from Python, we were notably ecstatic. This particular visualization using MatplotLib and numpy and geopandas illustrates the various waterways of Africa color coded to show the water basins to which they are connected— with a notable absence of color in the Sahara desert.
#### #1 Where 2020's Record Heat Was Felt the Most
##### Source: [New York Times](https://www.nytimes.com/interactive/2021/01/14/climate/hottest-year-2020-global-map.html)
##### Type: 3D Heatmap
One of the largest and high profile data visualization sources, the New York times created a literal 3D heat map that is both timely and important. Color coding squares of longitude and latitude, this 3D globe very acutely shows something climate scientists have been warning for decades: the rise in temperature is being felt most acutely where there are ice reserves in the north, which are experiencing a +6°C differential compared to the 20th century average. While we love where this is headed, we would love to see an animated version in the future.
### How Real-Time Operational Intelligence Can Improve AI Observability and Cut Costs
As AI systems move from experimentation into enterprise use, organizations are encountering a new class of operational risk: AI failures. Unlike traditional software, AI systems don’t fail in predictable or easily observable ways. Their most consequential failures often emerge in the *long tail of data*—rare, non-deterministic events that are invisible in averages or high-latency dashboards.
This challenge becomes exponentially harder as teams deploy multiple AI agents in parallel, each interacting with tools, APIs, models, and external data sources. To maintain trust, control, and compliance, and to ensure costs don’t rise due to AI failures, AI observability and governance must evolve beyond legacy approaches.
##### The Core Challenge: Traditional Observability Can’t See AI Failures
Traditional observability tools rely on sampling, aggregation, and delayed rollups. These techniques work well for infrastructure metrics like CPU usage, but they fall short for AI systems.
Some of those critical failures include:
- Hallucinations
- Prompt injection
- Cost overruns
- Tool misuse
- Unexpected agent behavior
These events are rare, contextual, and non-deterministic. When data is sampled or downsampled, the very signals that define AI risk disappear. The risk is magnified at scale. Dashboards slow down, data fidelity drops, and teams lose visibility into live system behavior precisely when governance matters most.
##### Why AI Governance Requires Real-Time, Full-Fidelity Operational Intelligence
Effective AI governance depends on three things:
1. Full visibility into all data
2. The ability to see all anomalies and emerging risks instantly
3. Human oversight at critical points in the workflow
Without access and visibility to live, detailed data, governance becomes retrospective rather than operational, and humans are removed from the loop until it’s too late.
##### How Row64 Enables Real-Time AI Observability
Row64 was built from the ground up to help enterprises see and interact with high-volume, low-latency data.
In enterprise AI, Row64 can capture all AI agent activity in real-time, with no sampling, no aggregation windows, and no missing traces. Its GPU/CPU-accelerated hardware stack, coupled with APIs for data streaming platforms like Kafka, delivers immediate AI risk information from across the organization in high-fidelity visual dashboards with sub-millisecond latency.
This includes:
- Every prompt
- Every tool call
- Every inference
- Every retry
- Every evaluation step
Because nothing is dropped or summarized, teams see actual AI behavior as it happens, not partial representations.
##### Observability Organized Around Agent Workflows
Given Row64’s operational intelligence capabilities, there is no need to flatten AI events into generic metrics. Instead, Row64 can organize them by agent workflows, letting enterprises compare step-by-step and model side-by-side to see which parts of the workflow are driving costs.
This workflow-based AI visualization enables you to understand how agents behave end-to-end, and where issues arise, without stitching together fragmented views across multiple tools.
##### Keeping Humans in the Loop
AI governance isn’t just about monitoring. It’s about keeping humans in the loop. With Row64, decision-makers can unearth anomalies as they occur, drill down from an overview to individual events, and intervene before issues escalate.
This approach ensures that critical decisions are made quickly, even at scale.
##### A Better Foundation for AI Operations
Row64’s performance, flexibility, and real-time data visualization layer provide a *new architecture for perception* — a platform built to deliver operational intelligence instantly, interactively, and visually across billions of data points, whether that’s [point-of-sale information](https://row64.com/blog/retail-optimization/), [fraud detection](https://row64.com/blog/blog-post-fraud-prevention/), [geospatial information](https://row64.com/blog/geospatial-landing-page-v33/), or AI systems.
As AI systems continue to scale, observability and governance can no longer be an afterthought. Full-fidelity, real-time streaming and visualization — paired with human oversight — is becoming a requirement.
Row64 is built for that future.
[Contact us](https://row64.com/company/contact-us/) for a demo.
### Row64 v3.5 Is Here!
Summer has been a busy time here at Row64. In addition to announcing [$4M in seed funding](https://row64.com/blog/announcing-our-dollar4m-seed-funding-round/), we launched a new website that includes [live interactive demos](https://row64.com/products/demos/) for a wide range of industries and use cases. All while being hard at work on a new release of our real-time operational intelligence platform, which we’re now ready to unveil: Row64 Version 3.5!
This version is packed with significant new features and performance enhancements, unlocking streaming workflows, file asset management, richer interactivity, and improved performance. Below is an overview of all of the latest updates. For a deeper dive, please have a look at our [release notes](https://app.row64.com/Help/V3_5/Release_Notes/)!
##### Streaming Support
- Create high-speed streaming workflows for inserting, upserting, and creating tables.
- Seamless Kafka and Delta Stream support, plus C++ Row64 APIs for custom device integration.
- Stream real-time dashboard updates with full cross-filtering support.
##### New File Asset Management Integration
- View, manage, and download file types including PDFs, DWGs, .glb, and .mp4 directly in dashboards.
- External files interact with all dashboard filtering, functions, and formulas.
- Customized file management via the JavaScript API
##### Speed & Performance
- New server GPU compute accelerates everyday data operations, such as SORT and GROUPBY.
- Enhanced server CPU compute multi-threading improves performance with massive datasets.
- A benchmarking CLI enables you to tune your server setup.
##### All New User Experience
- Streamlined UX simplifies dashboard creation and editing (Studio)
- Enhanced logs and error handling/messaging (Studio)
- Expanded markers and color themes.
##### Dashboard Enhancements
- Easier multi-column sorting and auto-resizing.
- Expanded Geo Selection UI, including Lasso and Shape Mask.
- Optional download of dataframes (.csv)
This is the culmination of months of work by our development team in collaboration with our pilot customers.
We're excited to hear your thoughts. Contact us to schedule a live demo or send any comments or questions to [support@row64.com](mailto:support@row64.com).
### Build Your Own Data Science Supercomputer
Have you ever wanted to build your own supercomputer? Yes?!Why wouldn't you!
If you want to break records and blaze through 100's of millions of data rows in a way that was previously impossible, consider DIY.
There's something exciting about bare-metal hardware, and with your own hands, putting together a data Ferrari that breaks records and does things that were impossible a year ago. Also, if you were ever a PC gamer, then this is easy because the heart of Data Science 2.0 is GPU & VRAM and builds similar to gamer rigs.
If you just want a ready-made machine, you could easily just go to the [CyberPowerPC](https://www.cyberpowerpc.com/) website and get a great build. But, for the adventurous, read on!
Ok, let's get right to the details of the build:
Overall, the build is just over $4K…Buying this build pre-made will likely be around double the price… anywhere from $8-10K.
You can get a great computer to run Row64 for $1.5K, but if you want to break records and push the limits of data, it's worth considering DIY.
Another factor is the price of GPU. The GPU is the heart of next-generation Data Science performance. So most of the money is going there.
If you consider the 3090 has an MSRP of $1.4K, then when prices come back down after this current cryptocurrency price surge, it will be about $800 less (than the above listing) to break records, especially when cheaper cards come out next year.
For a few of us, this was the first time doing a custom computer build - at Row64 we're mostly data scientists and coders.
So this was a bit of an adventure… First, we got out the motherboard and attached the CPU, the RAM & SSD, and then the CPU Cooler.
Overall it went pretty well, but we had to watch a few YouTube videos to make sure we were putting in the CPU and the Cooler in correctly. This was the intense exciting part of the whole process.
Next, plugged in the GPU and wired the motherboard to the power and case externally just to make sure everything was working.
It was an awesome moment when it first turned on!
On a side note, we should probably talk about the case… the big picture our goal was to make the fastest build possible, but not to use anything exotic or labor-intensive. So we took overclocking and water cooling off the list.
That meant we had to come up with the best possible solution for air cooling. Actually, we got quite obsessed with this topic and spent several months researching it. What we arrived at turned out incredibly well - the "Lian Li Lancool II Mesh Performance".
I can't say enough how awesome this case is. It's really great, because you can open it up from 4 different angles with 4 different hinges, and is just perfectly designed for incredible cooling and build flexibility. In fact, we brought our build over to our friends and Colorado neighbors at [LunarG](https://www.lunarg.com/) for a company field trip. These folks in Fort Collins are all incredible hardware engineers, who also are great GPU coders. When we brought out the build and were chatting about our Data Science Supercomputer, they liked all the ideas, but 95% of the conversation revolved around this case and how cool it was.
So, we pushed the cooling a step further than the case default. If you get the Lancool II Mesh Performance it comes with 3 fans. 2 in the front and 1 in the back. We bought an additional 3 Noctua fans and did lots of research on how to get optimal airflow for cooling. Row64 really pushes the GPUs, and we knew we needed great cooling if we wanted to break records.
As you can see our airflow setup is designed to draw air from the front and base of the case and push it out the back and top.
In all our tests afterward, this has worked perfectly and is a highly effective approach to cooling a cutting-edge GPU and CPU build.
Finally, we got all the parts together. Now it's time for the Data Science Supercomputer moment of truth… Turning it on!
It worked!
This is a great build for data supercomputing. At Row64, we've recently sorted over a billion records using this exact build and are pushing the frontier even farther with new coding techniques.
Let's take a quick look at what this all means compared to some of the other cards (actually CyberpowerPC & ASUS builds) we have in the office:
So there it is… A Data Science Supercomputer.
Over 53% faster than our 2080Ti, and it can sort over a billion records in 60 seconds (details coming soon).
And running Geo Ray-Tracing is beyond mind-blowing on this machine - but we'll explain more of that soon enough - it's for an upcoming blog post…
### What Is Operational Intelligence and Is It Right for Your Business?
For organizations operating in live environments, timing is everything.
Whether managing moving assets or dynamic systems, teams are making decisions in conditions that change by the second. In these environments, delays don’t just slow things down, they create risk, inefficiency, and missed opportunities.
This is where Operational Intelligence (OI) becomes essential.
Unlike traditional Business Intelligence (BI), which explains what already happened, Operational Intelligence is designed for what’s happening now, enabling teams to monitor, interpret, and respond to events in real time while keeping humans firmly in control.
Yet, even with some ability to see the operation in real time, most organizations face a hidden challenge\: **the Detection Gap**. The Detection Gap is the time between when a critical event enters the data pipeline and when a human operator can detect, interpret, and act on it. In fast-moving environments, even small delays can have oversized consequences.
The goal of Operational Intelligence is to eliminate the Detection Gap to achieve zero decision latency.
---
#### Why the Detection Gap Still Exists, Even with Operational Intelligence
Operational Intelligence should move organizations closer to real-time awareness. When implemented correctly, it should enable teams to monitor live data, detect issues as they happen, use visual analytics to understand events in context, including where they occur, and react immediately. For many, this would be a major step forward.
But organizations with some form of Operational Intelligence in place still experience the Detection Gap. Why? Because current systems are not designed to process the amount of high-velocity data in today’s world, nor provide instant spatial context, making them too slow and imprecise for operators who need to act in the moment.
To compound the problem, today’s organizations often have multiple systems to initiate responses to issues that arise. Each step introduces friction and an additional “hop,” which widens the gap between insight and action. In fast-moving environments, especially those involving physical assets in motion, those hops can be the difference between a minor adjustment and a major disruption.
In other words, closing the Detection Gap requires the ability to act on knowledge immediately.
---
#### How Row64 Redefines Operational Intelligence
Row64 is a real-time operational intelligence platform that extends beyond visibility, acting as an operator-facing layer that closes the gap between live data and operational decisions. By combining real-time processing, a unified operational canvas that keeps humans in the loop, and integrated APIs that connect to action and intelligence systems, it enables teams not only to understand what and where something is happening, but to act on it when it happens, without having to consult multiple systems for answers.
Row64 delivers this through three core capabilities\:
##### 1. End-to-End Real-Time Processing
Row64’s GPU-acceleration processes streaming datasets with sub-second end-to-end latency, eliminating delays and ensuring insights are always current.
##### 2. A Unified Canvas Keeps Humans in the Loop
Row64 brings together structured, unstructured, and spatial data in a single, real-time, interactive dashboard. No more jumping from one application or display to another. This unified “canvas” reduces the time for humans to interpret events, especially for assets in motion.
##### 3. Integrations That Enable Immediate Action
With robust APIs, Row64 connects insight directly to recommendations and execution. Once a decision is made, teams can immediately trigger workflows, dispatch resources, and initiate remediation.
These three capabilities create a continuous loop that monitors, alerts, and enables immediate action, effectively closing the Detection Gap.
While Row64 brings value to multiple industries, we’ve seen its greatest value in environments where a physical asset’s characteristics are rapidly changing, such as in\:
- Transportation and logistics
- Supply chain
- Utilities
In these areas, it's not enough to know something is wrong. Teams need to know where it's happening, what it impacts, and what to do next before opportunities are lost or the costs rise too high. For fleet operators, that means a routing decision based on live data rather than stale telemetry, which is the difference between a recovered delivery and a missed SLA. For logistics and supply chain teams, it means catching a disruption at the point of origin before it cascades through the network. For utilities and distributed infrastructure operators, it means detecting a grid or pipeline anomaly before it escalates from a containable incident into a system-wide outage.
---
#### Is Row64 Right for You?
Operational Intelligence is critical for organizations that need time-sensitive decisions. It reduces the Detection Gap, ultimately enabling faster decisions, improving operational performance, and reducing costs. But even Operational Intelligence can hit its limits if teams can’t process the data they collect fast enough, or must jump from one system to another to remediate the problems.
That’s where Row64 can help.
Row64 helps you act in the moment, with the context and confidence needed to keep operations moving.
[Let us show you](https://calendar.google.com/calendar/u/0/appointments/schedules/AcZssZ2RaGXr0NLZwjqzlvGvDdTpAx6FiNVZt6uVsfVDGfOYvncca53oGZg5saIPrmoGTrnZcEW5L48C) how Row64 can strengthen your operations.
### Unleash the Power of GPU Geo-Analysis For Business With Row64
In today's data-driven world, geo-analysis is emerging as a pivotal tool for up-leveling understanding past numeric data into *spatial* data. This exploration into geo-analysis not only enriches our decision-making processes but also unveils complex patterns and relationships that traditional data analysis methods may overlook. Studies have shown that visual information is [30 times more likely to be read](https://blog.csgsolutions.com/15-statistics-prove-power-data-visualization) than text and can result in a 5x increase in [decision-making speed](https://www.bain.com/insights/big_data_the_organizational_challenge).
Unfortunately, Geoanalysis has often been plagued by unresponsive, difficult-to-learn software. That’s why at Row64, we invested heavily in GPU-accelerated data visualization, including the fast Geoanalysis.
We break it down in our example below:
#### Our Product Features Include:
1. Fast Shape and GeoJSON Loader: Row 64 features the fastest-ever Shape and GeoJSON files, which allow you to drag files directly to load in milliseconds.
2. **Interactive Data Connection**: Connecting different data attributes (like linking location codes to specific geographic points) is straightforward, enabling dynamic visualization changes such as switching between temperature and precipitation displays. Our software simplifies the integration of various data sources so users can easily map out data analysis using Geo IDs, enhancing the accessibility of complex datasets.
3. **Enhanced Layering and Visualization**: Users can add and manipulate additional layers (e.g., state and county outlines) over their maps. The interface allows for reordering and toggling visibility of these layers, akin to graphic design software functionalities.
4. **High Performance and Speed**: The platform boasts high performance, which is particularly noticeable when handling large datasets such as mapping all buildings and streets in New York City. This speed comes from multi-threading, software optimizations, and GPU hardware acceleration from companies like AMD and Nvidia.
5. **Advanced Graphics Technology**: In addition to Vulkan, Row64 leverages the latest WebGL and Web Assembly in the browser and is supported via an innovative system of threaded compression, contiguous memory, and zero-copy memory optimization. This integrated approach results in exceptional responsiveness and visual fidelity.
6. **GPU-Optimized Operations**: Much of the data processing, especially that handling dense data, spatial transformations, or primitive data, is offloaded to the GPU. This optimized division of compute resources leads to significantly increased processing speeds.
These features collectively emphasize Row64’s capabilities in providing a robust, user-friendly, and efficient geo-analysis tool that enhances data visualization, analysis, and decision-making processes.
#### The Future of Geo-Analysis
The significance of geo-analysis in our interconnected world is undeniable. Beyond merely enhancing our comprehension of spatial data, it empowers diverse industries to operate with greater efficiency and adaptability. With the evolution of tools and platforms that facilitate geospatial analysis, its accessibility and utility are set to increase, firmly establishing geo-analysis as an integral component of decision-making processes across a broad spectrum of domains.
##### The Catalyst for Enhanced Decision-Making
At its core, geo-analysis equips stakeholders and decision-makers with a spatial perspective, enabling a visual interpretation of data about the physical world. For instance, companies can optimize delivery routes by analyzing traffic flow patterns, or governments can identify flood-prone areas unsuitable for future urban development projects.
Below are other examples of GEO Analysis for business Include:
- **Retail Site Selection:** Identifying optimal store locations by analyzing demographic trends, consumer behavior, and proximity to competitors for maximizing foot traffic and profitability.
- **Supply Chain Optimization**: Evaluate the geographic distribution of suppliers and customers to streamline shipping routes, reduce delivery times, and minimize transportation costs.
- **Targeted Marketing Campaigns**: Segmenting customers by geographic region to tailor marketing campaigns based on local preferences and trends, increasing campaign relevance and conversion rates.
- **Risk Management and Insurance:** Analyzing historical weather patterns, disaster-prone areas, and other geographic factors to refine insurance premiums and risk assessments for businesses and customers.
- **Market Expansion**: Study international markets by evaluating regional economic indicators, cultural factors, and market competition to develop effective expansion strategies.
- **Utility Network Management:** Map utility networks (water, electricity, gas) and usage data to plan maintenance activities, prevent service interruptions, and ensure efficient resource allocation.
#### Revolutionizing Real-Time Analysis
The proliferation of consumer data has ushered geo-analysis into the era of real-time insights. Real-time immediacy has become crucial for managing dynamic situations such as live traffic conditions, public safety, supply chain logistics, or fraud attempts. As big data becomes an increasingly competitive differentiator, the *speed* of analysis has become a limiting factor for many businesses—with a report by [CBER stating that 80% of companies that switch to real-time analysis see a revenue bump](https://kx.com/wp-content/uploads/2022/05/Speed-to-Business-Value-Research-Report.pdf). It’s estimated that a worldwide switch to real-time data analysis could unlock [$2.6 trillion in value](https://www.rtinsights.com/report-80-of-real-time-data-businesses-see-revenue-jump/).
In conclusion, as we further integrate geo-analysis into various facets of business and society, it becomes clear that understanding the geographical context of our data is not just beneficial—it's essential. The ability to visualize, interpret, and act upon spatial data in real-time opens up new horizons for innovation and efficiency, making geo-analysis an invaluable asset in the modern analytical toolkit. Whether enhancing public safety, optimizing urban planning, or creating more targeted marketing campaigns, geo-analysis applications are as vast as they are vital.
### Fleet Management: How Operational Intelligence Avoids Vehicle Downtime
Fleet managers don’t need more data. They need fidelity & insights.
From telematics platforms, GPS tracking, dashcams, maintenance logs, to compliance systems, modern fleet management generates massive volumes of data every second. Yet most fleet management teams still struggle to see what’s happening in the moment, explore it as it's unfolding, and act before a small issue becomes a costly disruption. If you consider that the average cost of downtime for a single truck is [between $448 and $760 a day](https://www.fleetmaintenance.com/shop-operations/shop-management/article/12062632/the-true-cost-of-vehicle-downtime) (per FleetMaintenance in 2025), the costs to a fleet can rise quickly.
That’s where **operational intelligence** changes everything.
#### **The Fleet Management Data Problem**
If you’re operating a fleet of vehicles (cars, delivery trucks, ships, etc), you’re likely facing one (or both) of these challenges:
##### **1\. You’re Living in Multiple Applications**
Your telematics platform handles tracking. Dispatching lives somewhere else. Dashcam footage is in another portal. Maintenance scheduling requires yet another system.
Instead of managing your fleet, you’re jumping between software tabs and systems. Those disconnected systems not only slow decision-making and make cross-referencing nearly impossible, but also increase the chances that issues get overlooked. Together, the challenges increase the risk of mistakes that result in vehicular downtime and higher operational costs.
Whether you’re managing logistics, last-mile delivery, or service fleets, the speed of your decision-making matters.
##### **2\. “Real Time” Isn’t Actually Real Time**
Many fleet dashboards claim to be “real-time,” but if vehicle positions update every few minutes, the updates are not happening live, especially if loading an entire fleet on a map slows performance to a crawl. By the time useful insights surface, the moment to troubleshoot problems or address incidents such as accidents, with downstream systems might have passed.
Fleet management software should enable action, not just out-of-date visualization.
##### **The Shift to Real-Time Operational Intelligence**
Row64 was built to eliminate these bottlenecks through its real-time operational intelligence platform, which was designed to stream massive datasets, generate insights and visualize results, without sacrificing fidelity, speed, or interactivity.
For fleet management, that means:
- You see your fleet in motion rather than in delayed batches
- Months of historical telematics data load instantly
- You can connect to downstream ai agents and action systems
- Your data, visuals, and analytics act as a fully interactive command center
Operational intelligence is not another reporting tool. It’s an interactive decision environment that enables:
- Incident response in seconds
- Proactive hours-of-service (HOS) compliance
- Real-time fleet health and maintenance
And all of that means less operational risk, few costly mistakes and greater competitive advantage of your business.
##### **The Future of Fleet Management Is Unified**
Fleet management doesn’t need more dashboards. It needs fewer silos. Row64 is built to put fleet data at your fingertips in one application at the speed, scale, and fidelity modern operations demand.
If your team spends more time switching between tools and waiting for updates than making quicker decisions to avoid costly downtime, it’s time to rethink your approach.
See what real-time operational intelligence looks like in action.
[Schedule a demo](https://calendar.google.com/calendar/u/0/appointments/schedules/AcZssZ2RaGXr0NLZwjqzlvGvDdTpAx6FiNVZt6uVsfVDGfOYvncca53oGZg5saIPrmoGTrnZcEW5L48C) to learn more.
### LiDAR Data Is Everywhere. Closing the Detection Gap Is the Real Challenge.
*Imagine you manage water infrastructure for a mid-sized city. A storm event hits. You need to know which corridors are flooded, which pipe segments are at risk, and where your crews should be right now, not after a two-day survey turnaround. Sensors have given you the information, but your current data infrastructure can’t deliver it *fast* enough. That gap is the Detection Gap, and closing it is becoming the defining infrastructure challenge of the next decade.*
---
LiDAR — the spatial sensing technology that maps physical environments in three dimensions using laser pulses — is at the center of this shift. A few years ago, LiDAR was a technology you read about in research papers and autonomous vehicle announcements. Sophisticated and impressive, but out of reach for most organizations. The hardware was expensive, fragile, and required specialists to operate it.
That's changed quickly. New generation LiDAR sensors have dramatically reduced both the cost and the complexity of deployment. Drone survey systems that once required six-figure investments are now available in complete kits costing a fraction of that, and the data they produce is richer, more precise, and more actionable than anything the previous generation could offer.
The global LiDAR market reflects this shift. According to Precedence Research (February 202the trend continues6), the market is valued at $3.53 billion this year and is projected to reach $17.8 billion by 2035, growing at nearly 20% annually. The aerial and drone segment alone accounts for 47% of the market, driven by applications in infrastructure inspection, urban mapping, and utility management**. **The bottleneck is no longer the sensor. It's the software layer between the data and the people who need to act on it.
#### New Sensors. A New Generation of Data
A number of innovations have made LiDAR hardware more accessible and LiDAR data more actionable.
**New solid-state LiDAR sensors** eliminate the rotating mechanical components that made earlier sensors bulky and expensive to maintain. The move to semiconductor-based manufacturing is doing for LiDAR what it did for cameras\: collapsing the price curve while improving resolution and reliability. Systems that cost tens of thousands of dollars several years ago can now be sourced for hundreds of dollars, and the trend is continues.
**4D LiDAR** adds velocity measurements to the traditional x/y/z point cloud, allowing sensors to detect not just where an object is but also how fast it's moving. Companies like Aeva are shipping this for production automotive programs today.
**FMCW (Frequency-Modulated Continuous Wave) sensors** are interference-resistant, which becomes important when multiple LiDAR units operate in the same environment, such as a busy logistics terminal, a smart intersection, or a construction site with dozens of active sensors.
**Long-range precision** is crossing thresholds that make infrastructure-scale sensing practical. That means centimeter-level accuracy at distances that previously required fixed survey-grade installations.
Taken together, these shifts mean that spatial sensing is becoming a standard operational capability rather than a specialized research investment. A single drone operator can now survey 20–30 miles of utility corridor in a single day — work that once required specialized crews and weeks of coordination.
#### Precise Mapping Becomes Practical
One of the most significant — and underappreciated — consequences of this technological innovation is its implications for mapping.
For decades, high-resolution spatial surveys were slow, expensive, and episodic. You hired a team, flew equipment, waited for processing, and got a map that might be days or weeks old by the time you could use it. That cadence was a fundamental constraint on how organizations managed physical space.
Affordable drone-mounted LiDAR is changing that model entirely. A survey that once required specialized contractors and significant lead time can now be conducted with consumer-accessible hardware on a regular or even continuous basis. This is making "micromapping" — the practice of capturing environments at the level of individual sidewalks, entrances, trees, street furniture, and structural details — practically achievable for the first time.
For any entity managing land, infrastructure, or public space, that's a meaningful shift. A city public works department can survey a corridor after a storm event and have updated spatial data within the hour. A facilities team can track construction progress in near-real-time. An emergency response team can receive instant post-disaster updates as conditions change on the ground, with live maps overlaid with sensor feeds and operational context, rather than waiting for a static report.
This last case — disaster response — illustrates what makes real-time survey capability genuinely different from anything that came before. When conditions change by the minute, the value of spatial intelligence lies in the ever-changing context that can show updates as crews move through the field. The data is no longer a periodic snapshot. It can be a near-real-time operational view of the physical world.
#### Mapping + Data Streaming in Real Time at Scale
Here's where a hard problem emerges. Processing dense mapping data, while keeping it interactive, geospatially anchored, and cross-filtered with other operational data, is technically demanding.
Most mapping and GIS solutions struggle with this processing load. They were designed to deliver spatial data through pre-rendered image tiles that update in batches (which is slow) and plugins that attempt to layer streaming data on top. In today's operational environments, solutions need to render the base map layer fast enough to simultaneously handle survey updates, live sensor feeds, and operational context without degrading it.
In the example below, Row64 can gather live water meter data and combine it with maps generated by drone-mounted camera & LiDAR sensors to show water meter readings and usage analytics for a specific neighborhood. This is only possible because Row64’s runtime is built on low-level performance optimizations throughout the entire processing pipeline, rather than the GPU-only rendering optimizations used by conventional tile-based mapping tools.\
\
#### Real-Time Operational Intelligence and Spatial Data
Row64 was designed from the ground up for exactly this class of problem\: high-volume, fast-changing data that needs to be understood and acted on in real time. It’s not a mapping tool or a GIS platform — it’s the operational layer that sits between raw sensor data and human decision-making, closing the gap between when data arrives and when an operator can act on it.
The reason this matters now is that the bottleneck in spatial intelligence has shifted. For years, it was the hardware. As explained above, sensors were too expensive, too fragile, and too specialized. That problem is largely solved. Now, the bottleneck is what happens between data arrival and decision\: the Detection Gap.
Row64’s GPU-accelerated runtime leverages LiDAR and camera survey information overlaid with sensor telemetry and operational context and delivers it in a single interactive view. The open API means organizations can bring their existing data sources and connect to their existing action and automation systems into the same operational picture without ripping out their infrastructure.
#### Mission-Critical Operations That Can’t Afford to Wait for Data
As LiDAR becomes more accessible, the organizations that will get the most out of it are those that can operationalize the data, not just collect it. City administrators tracking infrastructure change. Operations teams monitoring large outdoor environments. Emergency management teams that need situational awareness during and after disasters. Any organization managing physical space that has historically had to work with data already hours or days old.
The question most organizations haven’t answered yet is deceptively simple\: once the drone lands and the data uploads, how long does it take before an operator can see it, query it, and act on it? For most organizations, the honest answer is\: not fast enough. That’s the Detection Gap. And it’s solvable.
The hardware shift is real, and it's happening now. The question every operator should be asking is\: when the sensor data arrives, can you see and act on it?
If you manage physical infrastructure — utility networks, public works, emergency response systems, or large-scale outdoor operations — and you’re currently working from spatial data that’s hours or days old, we should talk. Row64 is purpose-built to close the Detection Gap between sensor data and operational decision-making. We’re actively working with utilities and infrastructure operators, and we’d be glad to walk through a live capability demonstration specific to your environment.
[Request a Demo](https://row64.com/company/see-a-demo/)** | **[Talk to our Team](pageid:ef2b99e5-7211-49d5-b85d-180b8cfd0060)
### Pepperdine Graziadio Business School Students Pioneer Course Streamlining Data Science Using GPU Technology
For many students at the Pepperdine Graziadio Business School, their Capstone course is a highlight of their graduate school tenure. A final course in their final semester that allows them to apply what they've learned in business school to real life projects.
While this class is often its own reward, students of the Summer 2022 "Analytics Education To Business" course received an additional bonus during their course this year: a chance to pilot brand new software that brings powerful GPU compute technology to the world of data science.
Led by Alfonso Berumen, Instructor of Decision Sciences and Information at the Graziadio Business School, the pilot program gave students exclusive access to Row64 software&mdasha program using the power of GPUs and Python scripting to massively increase the scale and reduce the load time for large data sets.
Armed with cutting edge software that allowed them to do large scale data manipulation up to 100 times faster than Excel, the students were tasked with working with datasets in the millions of rows to create data visualizations and use their experience to do competitive market analysis on emerging software to understand how to make a case for new tools and skill sets emerging in the data science world.
#### The Genesis of Collaboration
Pepperdine Graziadio Business School has built a name for itself in the business world, having many of its graduates recruited at the most prestigious investment banks in the world.
However, as the need for high level analytical thinking and data science beyond the realms of finance, graduates are increasingly finding themselves with exciting and novel opportunities &mdashbeyond strictly finance. Such was the case with Paul Trinh, a 2021 MBA graduate who now is Director of Product at Row64.
"I was introduced to Row64 by a then student of mine, Paul Trinh, who's now a part of the team at the company." said Clemens Kownatzki, Associate Dean at Pepperdine Graziadio Business School. "It took a few more months before I could get my hands on the tool with their first production version&hellipI was amazed about the speed. Something that might have taken Excel 20 minutes could be done in a *few seconds*."
With Associate Dean Kownatzki's approval to implement Row64 software in the powerful iLabs facility, the decision now had to be made about if there was an appropriate course to integrate the software into. For this, Alfonso Berumen, Instructor of Decision Sciences & Information stepped up, introducing Row64 to his cohort of 25 students for the Business and Analytics Capstone Course.
While Instructor Berumen and the Business Analytics Program Committee had previously turned down collaborations from other menu-based analytics software companies (which Berumen described as more apt for a middle manager role than the analyst role his students were being hired for), he identified two major benefits that attracted him to Row64: a native spreadsheet UI, and Python scripting abilities.
"I wanted to see what the hype was about. I've used so many different software like SAS, R, Python&hellipI wanted to sort of see how it stacked up because I was initially a little bit skeptical that you could just use a giant spreadsheet to analyze data and it would have the same functionality or the same abilities or capabilities as other options," said Berumen.
"The second main component is that it's Python-based. Being able to see the python code to be able to work with Python code and interface with that&hellipI mean right now the most popular data science language. I think that's a very important part of the Row64 software as well."
#### Staying Ahead Of The Skills Curve
Berumen's comments speak to an understood truth of the modern business skill set: working with large data sets, particularly with the use of Python, has become a requirement.
"\[T\]he finance industry is increasingly looking for finance graduates with coding skills," says Associate Dean Kownatzki. "If Python isn't on your resume, good luck finding a job in an investment firm these days."
The problem is that for many eager business school students, coding is a means to an end a task to labor through for the true reward of applying an analytical mind to a treasure trove of data. Many naturally business minded students struggle or suffer through intensive coding courses that have become common not for coding any program, but simply for sifting through data. Coding tests have become common, which is adding a technical barrier to key insights.
Instructor Berumen has noticed this in his cohorts. "There's definitely different abilities and different interest areas. So there's some students who may not be strong coders but still have the ability to analyze data effectively *if* provided with the right tool."
While Pepperdine maintains a strong emphasis on teaching students to learn how to code in Python (even offering a course exclusive to this), Row64 is on the frontier of the data science world, providing a powerful and simple to use alternative for students who find their forte is in insight. By outfitting the powerful iLab computers with Row64 (available for all graduate student use) to use for big data sets, Pepperdine is giving their students an extra skill and toolset over students from other Universities.
As the world increasingly harnesses the power of GPU compute, and large million row data sets grow increasingly common, new hires who can leverage the emerging technology in their benefit will have a head start on the jobs market.
As Associate Dean Kownatzki puts it, when it comes to staying ahead of the game in the business world, "technological innovation is always the biggest driver."
#### Judging By Students
The first pilot of its kind, both the faculty at Pepperdine and the founders at Row64 were eager to see how it would pan out. For his part, the pilot's leader Professor Berumen said the software seemed very intuitive.
"I didn't really see a significant learning curve. It seemed like pretty quickly they were able to pick up on how to use the software&hellip use the recipes." he said. "I think they relied a lot on those recipes. It seemed very straightforward from my perspective."
Berumen's synopsis appeared to be echoed by the students, many of whom commented on how technology like this would have reshaped their learning by doing so much of the leg work required of Python&mdashfrom data ingestion all the way through data visualization.
"Row64 merges every data analysts' favorite tool into one," said Megan O' Connor, one of the students. "\[It\] enhances data storytelling in ways not yet available"
"Row64 focuses on the frontier of speed and scale in data science with parallelization of the GPU '', said another student Nvyang Liu. "This software is outstanding for loading and processing massive data sets''
With unanimous approval from both the students and faculty responsible, the pilot between Row64 and Pepperdine serves as an example of the symbiotic potential of combining the brightest young minds of today, with the technology of tomorrow. A relationship that rewards both the students, and the software developers.
"There is so much promise coming out of these mutually beneficial projects," says Associate Dean Kownatzki. "All of this is very much in line with my vision to integrate companies in our educational offerings and to create pathways to learn from each other."
"In an ideal world, businesses will provide insights as to the skills and capabilities for success and we can in turn educate our students to develop into ideal job candidates&mdashhitting the ground running from day one.
### Understand Customer Sentiment Using Product Review Data
Understanding customer sentiment using product review data has become essential in understanding customer experience and improving products and services. A [study by Bain&Co](https://www.bain.com/insights/customer-experience-tools-sentiment-analysis/#:~:text=new%20revenue%20streams.-,During,-an%20economic%20recession) estimates that 80% of businesses use some sentiment analysis,
Internet usage has led to a dramatic rise in unstructured data in natural language forms, such as social media mentions, product reviews, web comments, and increasingly transcripts of videos such as YouTube, TikTok, or Instagram. This [presents opportunities](https://towardsdatascience.com/sentiment-analysis-concept-analysis-and-applications-6c94d6f58c17) and challenges for businesses seeking meaningful insights from large volumes of digital information.
Because of this, Row64 Dashboards has prioritized the quick and easy analysis of unstructured text. This is why our latest release makes sentiment analysis from varied text sources a breeze through the following mechanisms:
1. **Dynamic Data Interaction**: Navigate through large, complex datasets by zooming, panning, and selecting specific data points in real-time.
2. **Sentiment Analysis and Filtering**: Users can filter data based on sentiment—identifying the most positive or negative feedback within the dataset.
3. **Cross-Filtering for Deeper Insights:** Users can apply additional filters to compare data sources or types. For example, they can analyze sentiment from customer support interactions versus discussions on forums like Reddit. This helps pinpoint specific topics or issues, such as security concerns or product feedback, providing detailed insights into public opinion or customer satisfaction.
In the video below, we demonstrate how Row64 can easily visualize a massive unstructured data set so that we can zoom, pan, and select elements in real time to drill down into details.
This example immediately shows the benefit of being able to cross-filter by data source/type. If we consider only support team input, the sentiment is reasonably positive, *but* if we look at Reddit discussions (which tend to focus on health and security concerns), it’s much more negative.
Now, we have the tools to drill in and understand discussions in that area.
Within cross-filtered sets, we can find either the positive things being said or, conversely, highly negative topics being discussed. This is a fantastic tool for understanding a company's online reputation and drilling down into how customers and prospects speak of your company.
#### The Power of Unstructured Data
Unstructured data is akin to a sprawling, digital jungle—dense, untamed, but rich in resources. It diverges from the structured datasets neatly organized in rows and columns, presenting a formless mass of potential knowledge. Examples of this for word sentiment analysis include forum discussions, social media posts, blog articles, news comments, and customer support call transcripts or emails.
The treasure within unstructured data is its *depth of insight*. The real magic happens when businesses deploy [advanced analysis tools](https://www.techtarget.com/searchbusinessanalytics/definition/advanced-analytics) to sift through this data, identifying trends, sentiments, and patterns that pass unnoticed in traditional analyses.
When correctly decoded, it offers a nuanced understanding of customer behavior, market trends, and operational efficiencies. By leveraging the right technologies, businesses can transform this seemingly impenetrable data into actionable business insights, furnishing a competitive edge that is hard to replicate.
#### The Mechanics of Sentiment Analysis in Product Reviews
The core of sentiment analysis lies in categorizing the polarity of content into positive, neutral, or negative sentiments. This process involves sophisticated algorithms and techniques like Linear Regression, [Naive Bayes, and Support Vector Machines](https://www.analyticsvidhya.com/blog/2020/11/understanding-naive-bayes-svm-and-its-implementation-on-spam-sms/) (SVM).
These methods enable efficient categorization of product reviews, allowing businesses to glean insights from customer feedback quickly. Identifying [negation phrases](https://nealcaren.org/lessons/wordlists/) in reviews is crucial in understanding true sentiment, as these phrases often include a mix of adjectives, adverbs, and verbs that significantly alter the sentiment conveyed.
Another vital aspect is the [sentiment score computation](https://www.alpha-sense.com/blog/engineering/sentiment-score/), where each word or phrase's sentiment score is calculated based on its occurrence in reviews with different star ratings.
#### Interactive Data Exploration
The breakthrough in harnessing the power of unstructured data lies in the ability to interact with it dynamically. Rather than static tables or charts, innovative tools now allow analysts to engage directly with the data. Users can zoom, pan, and drill down into specifics through intuitive interfaces, pivoting their view based on real-time queries and interests.
Imagine the ease of pinpointing customer sentiments across different platforms with a few clicks or swiftly identifying topical trends that are shaping consumer discussions. This level of interaction doesn't just streamline data analysis; it morphs it into an immersive, highly adaptable process. Such capabilities are invaluable for businesses aiming to keep a finger on the pulse of their operational landscapes and market dynamics.
#### Enhanced Business Insights Through Cross-Filtering
Cross-filtering takes the potential of interactive data analysis a step further. It lets analysts layer multiple filters, dissecting the data across various dimensions—like time, sentiment, or source. This multidimensional analysis sheds light on complex questions, like how customer feedback varies between service channels or how product reception changes over time.
Consider the strategic advantage of comparing customer sentiments expressed in service interactions against the backdrop of broader social media discussions. This level of analysis can unveil discrepancies in public and private feedback, offering businesses a holistic view of their reputation and areas for improvement.
Such granular, cross-dimensional insights are revolutionary. They empower businesses to adapt strategies, products, and services with a clarity previously obscured by data's sheer volume and complexity. Cross-filtering turns the vast ocean of unstructured data into a navigable sea of strategic insights.
#### Final Thoughts On Sentiment Analysis
The landscape of data is evolving, and with it, the tools at our disposal to mine its depths. The ability to dynamically engage with unstructured data is not just an advancement—it's a paradigm shift. It bridges the chasm between modern technology's data-collecting capabilities and the actionable insights businesses crave.
In an era where data is the new currency, mastering the art of gleaning business insights from unstructured data is indispensable. Undoubtedly, it's a challenging frontier, but it's a realm brimming with opportunities to redefine business intelligence for those equipped with the right tools. The future belongs to those who can collect and store vast datasets and parse and understand them, transforming raw information into strategic assets that drive decision-making and innovation.
For data analysts and businesses ready to explore this unexplored terrain, the rewards promise to be as significant as the insights are deep. The era of tangible business insights from unstructured data is upon us, marking a new chapter in the annals of data analysis and business strategy.
### Physical Observability: The Missing Command Center for Physical AI
The physical world is becoming queryable. The question is\: who's watching the watchers?
For the past decade, software observability has transformed how we monitor digital systems. Logs, metrics, and traces turned opaque codebases into transparent, debuggable environments. If something broke in production, you usually saw it on a dashboard before a user noticed.
But in the physical world? You tend to find a problem only when something breaks. The physical world has been operating without real-time insight. They've been overwhelmed by sensor data, yet lacking real comprehension.
This is all rapidly changing.
---
#### What Physical Observability Actually Means
Physical observability isn't surveillance. It's not a camera feed. It's not another dashboard bolted onto a legacy SCADA system.
Physical observability is the ability to interpret, contextualize, and reason about physical-world signals across space and time, transforming cameras, sensors, telemetry, and external data into structured operational intelligence.
---
#### The Rise of Physical Observability
Physical observability is an emerging concept gaining traction among leading technologists and investors, and for good reason. Andreessen Horowitz named it one of their "Big Ideas for 2026," with a16z investing partner Zabie Elmgren arguing that the next wave of observability will be physical, not digital. In her words from the [a16z podcast on Physical AI](https://open.spotify.com/episode/0fi9o6w2n8xTH6tY8509ax) and the Industrial Stack, with more than a billion networked cameras and sensors deployed across the U.S. alone, the infrastructure already exists. What's been missing is the operational intelligence layer to make it all understandable.
Gartner's latest research reinforces this trajectory. Their March 2026 "Predicts 2026\: Physical AI Pushes I&O to the Edge" report projects that over two-thirds of enterprises will deploy edge AI by 2029, up from just 10% in 2025. By 2028, more than two-thirds of enterprise-managed data will be created and processed entirely outside the data center or cloud. The data isn't moving to the cloud. The operational intelligence has to meet the data where it lives.
The physical AI market itself is on a rocket trajectory, valued at roughly $5 billion in 2025, with multiple analyst firms projecting it will exceed $50 to $80 billion by the mid-2030s at compound growth rates above 30%. This isn't speculative. It's being driven by real deployments in manufacturing, logistics, defense, energy, and construction.
#### Why Now? Three Converging Forces
**1. The Data Explosion at the Edge**
IoT device deployments are growing at roughly 9% CAGR, with approximately 11.7 billion devices installed as of 2025, according to Gartner's forecast data. And here's the kicker\: as much as 90% of existing edge data goes unprocessed. That's not a data problem. That's an operational intelligence problem. The sensors are already deployed. The signals are already being generated. What's missing is the ability to fuse, interpret, and act on them in real time.
**2. AI Can Now Reason Across Modalities**
For years, cameras recorded everything and understood almost nothing. As the a16z podcast put it, it was like a well-meaning intern who takes great notes but can't tell you what actually matters. Modern vision-language models and multimodal AI systems have changed this equation. They can now interpret video, telemetry, environmental data, and geospatial context together. The AI layer has caught up to the sensor layer.
**3. Operations Are Distributed by Default**
Mining companies operate across remote regions. Logistics operators run distribution hubs that never stop. Energy companies manage assets across thousands of square kilometers. Construction sites are chaotic environments where conditions change hourly. Leadership simply cannot physically observe what's happening across these distributed operations. Scale without integrated visibility creates fragility. Scale with observability creates resilience.
---
#### The Command Center Problem\: Even AI Needs a Single Pane of Glass
The industry is rightly focused on AI models, edge compute, sensor fusion, and autonomous decision-making. But there's a fundamental truth that often gets overlooked\: even in a world of AI automation, you still need a visualization layer that brings it all together as a unified command center.
Think about it. You're running a construction site with AI-powered safety monitoring, autonomous equipment, environmental sensors, and asset tracking. Each system might be making smart local decisions. But who's watching the whole picture? Who correlates the safety anomaly on the west side of the site with the equipment movement pattern on the east side and the weather data that just came in?
Autonomous agents need orchestration. Multimodal sensor streams need correlation. Alerts need context. And human operators need a real-time, interactive visualization layer that renders all of this complexity into something they can understand and act on at the speed of the operation.
This is where platforms like Row64 become essential infrastructure in the Physical AI stack. It closes the Detection Gap between signal and action.
---
#### Row64\: Closing the Detection Gap with Real-Time Operational Intelligence
Row64 is a GPU/CPU-accelerated operational runtime engine for real-time decisions, purpose-built to address the exact challenges posed by physical observability. Built from the ground up on a high-performance computing stack that leverages WebAssembly and WebGL, Row64 delivers interactive analysis of location data, live streaming data, and warehouse data for context with sub-second latency.
Here's why this matters for Physical AI\:
**Real-Time Streaming at Scale.** Physical observability generates continuous data\: sensor telemetry, camera feeds, equipment status, environmental readings, AI inference outputs. Row64 can ingest and visualize all of this in real time, with no sampling, no aggregation windows, and no dropped events. When a safety anomaly, equipment failure, or unauthorized access event occurs, teams see it as it happens.
**Multimodal Data Fusion in a Single View.** Physical AI environments generate wildly diverse data types\: structured telemetry/data, geospatial coordinates, images and more. Row64's GPU-rendered platform can composite all of these into a single, interactive view. This is the "single pane of glass" that physical operations actually need. Not a simplified dashboard that hides complexity, but an interactive operational intelligence surface that reveals it.
**GPU-Accelerated Geospatial Analysis.** For industries like fleet management, supply chain, and utilities, the geospatial dimension is fundamental. Row64's GPU-accelerated geo-analysis enables operators to visually explore infrastructure networks, asset locations, and environmental conditions across entire regions, zooming from a continental view down to a specific asset, transmission line, pipeline segment, or building footprint, all at interactive speed.
**The Aggregation Layer for Alerts and Decisions.** As enterprises deploy more AI agents and autonomous systems at the edge, they face a new problem\: alert fatigue and decision fragmentation. Each AI system generates its own alerts, recommendations, and actions. Row64 serves as the aggregation and correlation layer, pulling together outputs from multiple AI systems, sensor networks, and operational data sources into a unified command center where human operators maintain oversight and can intervene when needed.
**Edge-to-Cloud Flexibility.** Gartner's research emphasizes the growing importance of IT/OT alignment and the shift toward edge-first architectures. Row64's flexible deployment model supports on-premises, cloud, and hybrid configurations, meaning it can sit wherever the data demands. Whether that's a control room in a refinery, a mobile command center at a construction site, or a cloud-based operations center monitoring distributed assets globally.
---
#### The Samsara Analogy, But Bigger
The a16z podcast drew a powerful analogy\: when Samsara entered the freight industry, just having a single dot moving on a map seemed revolutionary. The operational gains were enormous, all from basic visibility alone.
Now, imagine the next step function. Instead of a dot on a map, you have a live, multimodal understanding of an entire environment\: where assets are, what's changing, what's risky, what needs action. That's the promise of physical observability. And it requires a visualization platform that can actually render that level of complexity in real time.
Row64 isn't trying to be the AI. It's the command center where sensor fusion becomes situational awareness. Where edge intelligence becomes operational intelligence. And where human judgment meets machine perception.
---
#### The Bottom Line
Physical AI is one of the defining technology trends of the next decade. But AI models running at the edge are only part of the story. Without physical observability (the ability to perceive, contextualize, and react to the physical world in real time) those AI systems are flying blind.
And without a real-time visualization and command center layer to bring it all together, organizations risk falling into a Detection Gap that blurs the operational picture when it matters most.
The question is\: **do you have the command center to see it all?**
### Unveiling The Future of Geospatial Dashboards
Geospatial analysis is the *hidden gem* of business data. From consumer purchasing trends to natural disaster risks to figures on income and employment, geospatial data is the frontier of data science, immediately letting you visualize data points *where* and *when* they happen.
It’s no wonder that the industry is expected to **grow at a** [**CAGR of 17.7%**](https://www.prnewswire.com/news-releases/geospatial-analytics-market-size-to-grow-by-usd-102-97-billion-from-2022-to-2027--the-increasing-adoption-of-geospatial-data-analytics-in-the-healthcare-and-insurance-sectors-to-drive-the-market-growth---technavio-301822479.html) between now and 2027.
However, there remain three main problems with geospatial data:
1. It’s a complex skill to master
2. The software doesn’t handle large data sets
3. The software can’t handle diverse data types
We’ve solved all three of these with our first-ever interactive, in-browser dashboard, which can visualize millions of records in real-time.
Our newly released version features the following abilities:
##### Cross Filter Large Data Sets In Browser
Real-time cross-filtering smoothly handles 44 *million* records in the browser.
##### Dynamic Record Inspection
See map *and* cell data dynamically respond to filters attribute filters.
##### Time Lapse Animation
Create interactive time-lapse visualizations, as shown below, using weather data.
##### Check out the full video below
Are you interested in a demo of how our dashboard can help you easily manipulate your businesses’ geospatial data? [Click Here](pageid:ef2b99e5-7211-49d5-b85d-180b8cfd0060) to contact us for more information.
### Row64 and Carahsoft Partner to Deliver Real-Time
Operational Intelligence to the Public Sector
**CHEYENNE, Wyo., and RESTON, Va. – July 1, 2026 – **[Row64](http://www.row64.com/), developers of the operational intelligence solution for real-time decisions, and [Carahsoft Technology Corp](https://www.carahsoft.com/)., The Trusted Government IT Solutions Provider®, today announced a partnership. Under the agreement, Carahsoft will serve as Row64’s Public Sector distributor, making the company’s real-time technology available to the Public Sector through Carahsoft’s reseller partners and NASA Solutions for Enterprise-Wide Procurement (SEWP) V, Information Technology Enterprise Solutions – Software 2 (ITES-SW2) and OMNIA Partners contracts.
“When a crisis unfolds, operators can’t wait for a dashboard to refresh – they need to see what is happening and act on it now,” said Marc Stevens, CEO at Row64. “We provide a real-time operational layer that closes the Detection Gap between event and action, giving operators a live picture of events as they unfold so they can detect, understand and act with zero decision latency. Through Carahsoft’s reseller network, agencies can deploy these capabilities without procurement friction.”
Row64 helps organizations detect and respond to events before delays become financial or mission-critical problems. For grid operators, field coordinators, emergency managers and logistics supervisors, the challenge isn't a lack of data, it's turning that data into timely, actionable insights. While data pipelines have evolved rapidly, the operational layer has struggled to keep pace. Row64’s GPU-accelerated platform combines event data with location and operational context in real-time to deliver a unified picture to any operator, in any browser.
Every Row64 capability is API-accessible, enabling seamless integration with agentic platforms and action systems. This allows organizations to detect disruptions at the source, initiate immediate responses and automate operational workflows while keeping humans in the loop without replacing existing infrastructure.
Row64 supports a wide range of use cases, including utility and infrastructure monitoring, supply chain operations, fleet and transportation management, drone mission oversight, emergency response coordination and real-time sensor and operational dashboards.
“Row64’s real-time operational intelligence capabilities empower Government operators with actionable insights,” said Lacey Wean, Program Executive for Smart Cities Technology Solutions at Carahsoft. “By delivering critical data visualization and eliminating decision latency, Row64 enables operators to detect, understand and respond to events as they happen. Together with our reseller partners, Carahsoft and Row64 are bringing a modern operational intelligence platform to the Public Sector, helping agencies make faster, more informed decisions.”
Row64’s solutions are available through Carahsoft’s SEWP V contracts NNG15SC03B and NNG15SC27B, ITES-SW2 Contract W52P1J-20-D-0042 and OMNIA Partners Contract #R240303. For more information, contact the Carahsoft Team at (844) 722-8436 or [Row64\@carahsoft.com](mailto:Row64@carahsoft.com). Learn more about Row64’s solutions [here](https://www.carahsoft.com/row64). \
\
Or [Contact Us](pageid:ef2b99e5-7211-49d5-b85d-180b8cfd0060) for more information.
### Then Is Now - A6000 Reality Engine
Nvidia released the RTX A6000 in October 2020.
With a mind-blowing 48GB of VRAM and the Ampere architecture, this card is the best combined visualization & compute hardware ever made in human history (at least as of today).
Some may say the A100 is faster - but it doesn't have raytracing. Some may say the 3090 has 1% faster ray-tracing performance - but it doesn't have the VRAM scale. The A6000 is really the top card for the frontier of combined visualization and compute at large scales.
Row64 works closely with NVIDIA and is constantly driving towards squeezing every drop of performance we can out of low-level hardware and software. So we were very excited to get ahold of this card.
When it arrived, it was so new that there was no branding on the box… just a plain white box with bubble wrap.But inside was a RTX A6000!
Pretty exciting I was holding the most powerful graphics card in the world.
It brought back a lot of memories… Back in the summer of 1993 I was an intern working for the Canadian Department of Public Works - inside their Digital Simulation Lab. This was one of the most cutting edge computing visualization research centers in all of Canada. I just got the job by chance, noticing it on the job board at University of Waterloo. That was the last time I had been so close to the top visualization power in the world.
Part of my job was helping purchase a million dollar computer… Who knew a computer could cost a million dollars Canadian? It was going to be used to model the parliament buildings of Canada and visualize new infrastructure phases over time.
The computer itself was called a "SGI Onyx - Reality Engine 2" and cost 500K.
The RAM for the computer cost and additional 500K. There was so much RAM that a different word I had never heard of was used to describe it - a GIG.
We were paying 500K for 1 GIG of RAM. I told all my technical friends about it… everyone had the same response. "What's a GIG?"
Personally I just did little odd jobs around ordering the purchase. Coordinating the cooling for the room. Making sure the power was setup right. Answering questions about it when my boss was out. When it arrived I was not allowed to touch it or go near it. Only the top senior Graphics Operator could use it…
But out of the corner of my eye I was always watching what was happening on the screen. It was mind blowing.
Basically a 3D world could be navigated in real-time. A textured real world could be manipulated and moved around at 30 frames per second.
To me this was a total shock. I had some friends who did 3D renders on PC back then and they typically took 3 days to a week to render. Here it was happening as though time was nothing. Like the computer could see.
Looking down at this A6000 I could feel that something like this was happening again.
At Row64 we had been coding with real-time raytracing for a while at this point. To me, real-time raytracing is the only thing I've seen in computer history that had this massive impact on that Reality Engine 2.
Real-time raytracing puts you into a visual world, but one far better than any traditional rasterization technology. It can be up to 2000X faster than CPU raytracing. So it has this same effect of drawing you into this new real-time visual experience.
It's also interesting to think about how much the graphics hardware has changed but is still kind of the same:
[Here's a link](https://slideplayer.com/slide/13036745/) to an interesting deep dive into how graphics hardware has evolved over this 28-year span of time.
In general, both are using geometry engines or Graphics Processing layer that passes data to a rasterization layer.
The NVIDIA Ampere architecture however brings in many new core capabilities: CUDA Cores, Tensor Cores, and Ray Tracing Cores to name a few.
Let's look at the overall value change over this 28-year period:
Clearly, time travel forward in time is an outstanding strategy to get incredible graphics performance for a better price.
Note that the RTX speed of raytracing is at the same number of triangles but with raytracing instead of rasterization. A mind-blowing step forward in visualization quality… it's literally 1000X better. And keep in mind in a single frame in this example the A6000 is sending 8 rays and 16 bounces for every pixel on the screen - at 100fps.
Something interesting happened if you look back on the next step of the Reality Engine in history. SGI basically collapsed and the top brains working there went on to change the world of computers. Today's Supercomputer is tomorrow's computer, so people working in supercomputing get an early sense and experience of what the next generation of technology will look like. That's exactly how the Web Browser & Graphics Card revolutions started.
So there you have it. We're living in a moment in time that is much like 1993. An incredible new technology – real-time raytracing & compute has shattered the traditional barriers that were slowing down computers. Turning work that took weeks into milli-seconds.
It's an exciting moment and we can't wait to see what happens next. What new revolutions in computing will these new capabilities lead to?
On a side note, here at Row64 – we had a great time putting together this A6000 build. We found it interesting to integrate the top data science card with some popular gamer build techniques. For example, check out our build with a Noctua NH-D15 CPU Cooler:
It really worked out great and lead to all the insights we've put in this article.
If you're interested in the build details - please check out our article [How to Build Your Own Supercomputer](/blog/build-your-own-data-science-super-computer). Basically, the part list is identical just using an A6000 instead of a 3090.
Attribution - Geometry Engine Board:
[By Retro-Computing Society of Rhode Island - Own work, CC BY-SA 3.0](https://commons.wikimedia.org/w/index.php?curid=8290631)
[Attribution - SGI Onyx2 visualization system:](https://sgihardware.tumblr.com/post/175649548387/sgi-onyx2-visualization-system-introduced-in)
### Data Center CPU Trends
With the rapid growth of artificial intelligence, most discussions about data center hardware have focused on GPU trends. However, if you take a closer look, you'll find equally fascinating developments in CPUs that we explore further in this short video.
### Power Grid Operations: Crisis to Solution in Seconds
Picture a control room during a live outage. Alerts are firing across substations, and the data needed to make sense of it all is scattered across a dozen disconnected systems. That moment defines whether an outage stays contained or cascades, and it’s what Row64 is designed to solve. Row64 is a live operational intelligence platform built for operators in fluid settings (like grid operations), giving them a unified view of all critical data and the ability to act on it quickly, without switching between tools.
##### Scattered Data. Slow Response.
In 2024, U.S. outage restoration times (CAIDI) averaged roughly 7.4 hours — a 10-year high, driven largely by Hurricanes Beryl, Helene, and Milton. (EIA Form 861). The grid is getting harder to manage, which makes faster detection and response not a nice-to-have, but a necessity.
For many grid operators, the issue isn't data volume — it's fragmentation. SCADA, outage data, weather feeds and maps, and asset telemetry are scattered across different systems. Understanding it in a crisis depends on how quickly an operator can stitch it together and make sense of it. Those moments are rarely static. A single event can cascade to neighboring stations within minutes, and the longer it remains unsolved, the more customers it affects. We call that lag between event and action the Detection Gap, and closing it is the difference between an outage that stays contained and one that cascades.
##### One View. Instant Action.
Minimizing the time to action means speeding detection and understanding from minutes (or worse, hours) to seconds.
Row64 pulls live data from SCADA, outage management systems, weather feeds, and asset telemetry into a single GPU-accelerated view — with live telemetry overlaid on your service territory and one-line diagrams, not buried in rows of tables — while also connecting directly to the downstream tools operators need to take action. Instead of switching between systems, operators see the full picture and respond from one place. When a fault occurs, operators instantly see which assets are affected, which customers are impacted, and which systems to engage. Because Row64 also connects via APIs to downstream systems, operators can instantly act to resolve issues from one place, without switching tabs or displays.
##### Demo it for yourself
Explore our Row64-powered live [Grid Ops Walkthrough](https://app.arcade.software/share/mevK92lCqWnTDUaDXbzS).
### Row64 and Northern Lights Announce Strategic Partnership
Row64 and Northern Lights have announced a strategic partnership focused on helping organizations turn live data into immediate, informed action in mission-critical environments.
As operational complexity increases and response windows continue to shrink, organizations need more than dashboards and alerts — they need real-time operational intelligence that operators can act on instantly. This partnership brings together Row64’s real-time operational intelligence platform with Northern Lights’ deep technical expertise and white‑glove implementation services.
#### Closing the Detection Gap
Row64 provides the operational layer that closes the “Detection Gap” between live data and operator response. By unifying an operational runtime engine with a dynamic visual canvas and open APIs, organizations can respond faster and more effectively when seconds matter.
#### Combining Platform and Delivery Expertise
Through the partnership, Northern Lights will support Row64 deployments by bringing enterprise delivery rigor, industry knowledge, and hands-on implementation experience. Together, the companies enable organizations to operationalize real-time intelligence in complex, high-velocity environments.
*“By partnering with Row64, we are extending our ability to help clients turn live data into immediate, informed action—where seconds truly matter,” said Wenjie Wang, President of Northern Lights. *
*“Northern Lights brings the experience and deployment rigor needed to deliver value quickly in environments where the cost of delay matters,” said Marc Stevens, CEO of Row64.*
#### Supporting Mission-Critical Operations
The partnership supports Northern Lights’ work across Taylor Corporation’s 38 business units and expands joint solutions for customers across industries including logistics, utilities, industrial operations, financial services, and smart cities.
Together, Northern Lights and Row64 are helping organizations move from insight to action — in real time.
To learn more about the partnership and what it means for your business, [contact us](pageid:ef2b99e5-7211-49d5-b85d-180b8cfd0060) today.
### Enter the Future of Data Visualization with Row64
With visual information processed roughly 60,000 times faster than text, compelling data visualization tools have the potential to increase understanding of everything from economic analysis to consumer trends.
That"s why in our recent product update, we enhanced Row64"s capabilities to meet the growing need for better visualization tools. By harnessing the GPU, Row64 delivers stunning data visualizations at the click of a button—operating at a speed, scale, and refinement not possible with other popular visualization tools.
Highlights include:
- Reveal and Time Series Animated Charting
- Subtle Gradients, Slightly Rounded Corners, and Cutting Edge Color Palettes
- Essential 2D and 3D Business Graphics
- Real-Time Responsive Heat Maps
- Enhanced spreadsheet autocomplete functionality
Whether you're creating high-quality business graphics or conducting in-depth technical analysis, Row64 data analysis and storytelling tools will give you the power you need to handle modern data projects.
Check out some example visualizations by [downloading](https://app.row64.com/Download) and get started today.
### Integrated GPUs and the Rise of the GPU Dashboard
One of the biggest untalked-about innovations in internet history?
WebGL support. WebGL is so common nowadays that most people don’t even know they’re using it.
Have you ever :
\-rendered a Google Earth landscape in insane detail? That’s WebGL.
\-ditched PowerPoint and Illustrator entirely for the quickness and ease of a web-based tool like Canva. That’s WebGL.
\-previewed a 3D render of a home or product in-browser? Played a game like Quake 3 online? Yup, also WebGL.
This uniform ability to render and interact with high-quality graphics in-browser without plugins has already changed the face of the web. Now is the time to bring that speed and visual quality to business intelligence applications.
We’re now in the era of WebGL 2.0 and powerful GPU dashboards.
Before 2018, dashboards were constrained to HTML and Javascript. Since then, integrated GPUs from the big three hardware manufacturers (Intel, AMD, and Apple) and the adoption of the WebGL 2.0 API from the big three browser creators (Google Chrome, Apple Safari, and Microsoft Edge) have unleashed a caliber of previously unimaginable browser-based dashboard quality and performance.
This new generation of GPU-accelerated dashboards allows us to
\-Drill down to record-level detail with millions of point-of-sale records
\-Visually explore the 44 million cell towers worldwide on a map in real-time
\-Run time-series animations of weather and cross-correlate crop growth
\-Select segments of roadways in large cities like Seoul - 13.5 million
\-Perform skill flow analysis with org charts of tens of thousands of employees
\-Track player movement through levels in a multiplayer game in real time
\-Automatically cross-filter structured and unstructured data types
\-Instantly display complex formula analysis in spreadsheet-style views
This level of visibility and shareability means everyone, from data analysts to data scientists to sales managers to C-level executives, can quickly and easily view the same information interactively at a greater scale and level of detail with the same accessibility.
### GPU Selection is Revolutionizing Dashboards
When it comes to data, seeing is understanding.
This is a foundational principle of Row64 that we’ve stayed true to. We have used every technique at our disposal to make data analytics and visualization real-time responsive—no matter the scale.
Why real-time? Besides saving companies hundreds of hours annually from dreaded “software lag,” real-time responsive data allows you to select and drill down to record-level detail at the speed of thought. No more wasting high-end talent to summarize/sample large data sets or cobbling together different programs to get results.
The video below highlights several compelling use cases of how this can be implemented in a business context:
1\. A lasso tool is used to select parts of a power grid across California and find the operational status, ownership, and municipality of all transmission lines in the state.
2\. Zoom in on Korea’s 13.5 million roads to get real-time data on concentrations of restaurants, bars, etc., for demographic research.
3\. Data manipulation with massive organizational charts to visualize the impact of acquisitions, understand skill flow in and out of the organization and efficiently do strategic workforce planning.
There is truly no limit to the scope or utility of real-time data.
### Row64 At GTC 21
\[UPDATE\] - Added after GTC ended. Here's the Row64 "The Data Decision Loop" GTC presentation:
Join Row64, [LunarG](https://www.lunarg.com/), and [Imaginary Spaces](https://imaginary-spaces.com/) at NVIDIA GTC
Dear Friends of Row64,
Join Row64 at NVIDIA's GTC for a transformative global event that brings together brilliant, creative minds looking to ignite ideas, build new skills, and forge new connections to take on our biggest challenges. It all comes together online April 12 - 16, kicking off with CEO and Founder Jensen Huang's keynote.
Row64 would like to personally invite you to attend Row64's "The Data Decision Loop" session at GTC to discover new research in GPU data science & raytracing and to network with a global community of developers, researchers, engineers, and innovators.
We will also show cutting edge new R&D done in collaboration with LunarG and Imaginary Spaces.
At GTC you can explore:
- Inspiring presentations from industry pioneers
- Startup insights
- DLI Training
- Demos
Registration is free and gives you access to all the live sessions, interactive panels, demos, research posters, and more. You can also add a Deep Learning Institute (DLI) full-day workshop to your GTC conference pass for just $249 each. NVIDIA's DLI offers hands-on training in AI, accelerated computing, and data science to help developers, data scientists, and other professionals solve their most challenging problems.
Don't miss out on this amazing event. Register at www.nvidia.com/gtc and be the first to know what's happening in the world of AI.
To register for "The Data Decision Loop" seminar:
1. Go to www.nvidia.com/gtc and sign up.
2. Click on this link: [GTC'21](https://gtc21.event.nvidia.com/media/The%20Data%20Decision%20Loop%20%5BS31871%5D/1_bb7fzk7d)
To join the talk with live chat, please return to the link: on **April 12th at 10 AM PDT** (make sure you are logged into participate).Otherwise, you can visit it anytime after that time to see the recorded version.
Cheers and hope to see you there,The Row64 Team
### Announcing our $4M Seed Funding Round
We founded Row64 to help enterprises process, understand, and act on today's ever-increasing volumes of rapidly changing data, enabling leaders to make decisions with clarity and confidence. I’m excited to share that Row64 has raised a $4 million seed round led by [Galaxy Interactive](https://interactive.galaxy.com/), with additional participation from [Alumni Ventures](https://www.av.vc/funds/aifirst) and [Differential Ventures](https://www.differential.vc/) as fuel to do just that.
This is a significant milestone for our team. The funding and customer traction validate our belief that real-time business intelligence, including high-quality interactive visual analysis for massive datasets, is no longer a luxury—it’s a necessity.
In today’s fast-paced, data-driven world, businesses are drowning in information. What good is all that data if you can't make sense of it when it matters most? Legacy business intelligence systems can’t keep up. They weren’t designed to handle the volume, velocity, and variety of data that has become the “new norm”. This is the problem Row64 was built to solve.
We've created the only visual real-time business intelligence platform for big data that empowers enterprises to interact with and analyze billion-record datasets instantly. Row64’s GPU- and CPU-accelerated computing stack processes and updates data at sub-millisecond speeds while powering a new generation of highly interactive dashboards built for today’s high-volume and fast-changing data. It allows companies to see changes in real time so they can act on opportunities and address challenges when they matter most.
Built from the ground up to push the boundaries of scalability and real-time performance, Row64’s Web Assembly (WASM) and WebGL native browser front-end brings GPU-rendered dashboards with detail and accuracy, capable of compositing rich data such as 2D, 3D, and GIS data to users across the entire organization.
What this means for you is:
- **Real-time analysis:** Our hardware-accelerated computing stack handles high-volume, rapidly changing datasets, allowing you to react to changes as they happen.
- **Unlimited visual exploration:** Seamlessly drill down from summary to record-level detail with zero-lag interactive dashboards.
- **Easy integration:** Industry standard APIs and interfaces make Row64 open and extensible, fitting seamlessly into existing IT environments and data pipelines.
We’re previewing our new [DeltaStream](https://www.deltastream.io/) Apache Kafka real-time streaming pipeline at the Databricks Summit in San Francisco on June 9-10, 2025 (Booth #F615), showcasing our commitment to low-latency, high-throughput streaming workflows.
I'm incredibly proud of our team's accomplishments and excited about Row64's future. We're not just building a platform, but a new standard for real-time business intelligence. We're enabling businesses to operate in today’s high-velocity environments with clarity and confidence.
Thank you for joining us on this journey. I can't wait to see how you use Row64 to transform your business.
\-Marc Stevens, CEO Row64
## Root Pages
> Pages at the root of the website
### Detect-Understand-Act in Real-Time
When time-to-action is critical, and waiting is not an option.
#### Logistics and Fleet Management
The delivery of goods is big business, but delayed deliveries, errors, and poor route planning can harm a logistics company’s reputation and carve into its profits. Access to historical and operational data enables real-time actions and decisions that bring goods to people faster, more safely, and at lower cost.
- Track every vehicle, shipment, and asset on live maps
- Reroute around delays and respond to incidents
- Optimize operations with location-aware, real-time intelligence
[Try Demo](/products/demos/?demo=fleet-and-logistics)
#### Utilities
Keeping utilities online is critical for delivering essential water, power, and services to people across the globe. From identifying bottlenecks, inefficiencies, and underperforming assets to quickly detecting and addressing power outages, interruptions, and potential disasters, real-time intelligence is vital to keeping services online and operational.
- Monitor grid operations and infrastructure status
- See outages live on maps
- Restore service faster
[Try Demo](/products/demos/?demo=grid-operations)
#### Retail and E-Commerce
Online and brick-and-mortar stores collect transactional sales data daily. Real-time SKU-level intelligence, interactive floor layouts, and prescriptive recommendations help retail organizations adjust promotions, pricing, and product placement on the fly, uncovering new revenue opportunities and cost savings as soon as they become available.
- Track inventory, sales, and metrics in real-time
- Identify stockouts and loss issues as they occur
- Optimize labor deployment with live foot traffic data
[Try Demo](/products/demos/?demo=retail-price-optimization)
#### City Intelligence
City officials are responsible for the health and safety of the populations they serve. Whether plannin smarter cities, monitoring traffic in real time, or responding to incidents, access to structured, unstructured, and live event data can make cities more efficient and safer for citizens while enabling managers to deliver on their promises.
- Integrate traffic, public safety, and other data on unified maps
- Coordinated response to incidents
- Optimize city services in real-time
[Try Demo](/products/demos/?demo=garden-city)
#### Telecommunications
Modern 5G and 6G networks require careful monitoring to ensure the network's steady operation, which, in turn, fuels customer satisfaction. Real-time operational intelligence that turns raw, captured data into actionable insights for network operators, engineers, and data scientists, leading to smarter decisions and better outcomes for both customers and the business.
- Monitor infrastructure health live on spatial maps
- Detect service degradation instantly
- Maintain SLA compliance in real-time
[Try Demo](/products/demos/?demo=cell-towers)
#### Finance and Banking
Financial markets move in seconds. Seeing rates and currency changes happen in real time enables these institutions to react quickly to market fluctuations, detect issues before they become catastrophic problems, and provide better overall service to the millions of people who entrust them with their money.
- Real-time anomaly detection
- Market forecasting
- Faster and more personalized services
[Try Demo](/products/demos/?demo=credit-card-fraud)
#### AI Observability & Governance
As more enterprises adopt AI systems, new and hard-to-detect risks are emerging. AI failures often occur in rare, non-deterministic moments that traditional observability tools miss due to sampling, aggregation, and delayed dashboards. A real-time data visualization layer enables decision-makers to see every prompt, tool call, and inference as they occur, keeping humans in the loop and preventing costly failures.
- Monitor autonomous AI agents and systems in real-time
- Visualize decision chains and track actions taken
- Trigger interventions when agents operate outside parameters
[Try Demo](/products/demos/?demo=ai-observability)
#### Cyber Security
Threats to digital security are everywhere and only growing in size and frequency. Capturing data from network traffic, log or endpoint data, threat intelligence feeds, and more in real time for precise analysis enables security teams to respond at a moment’s notice and keep businesses safe.
- Identify threats the moment they emerge
- Correlate across systems in real-time
- Trigger automated responses
[Try Demo](/products/demos/?demo=cyber)
#### Healthcare
In healthcare, where lives depend on operational intelligence, hospitals and clinics can’t afford to operate on outdated data. The better information operations teams understand what is happening with their staff and medical equipment, the more efficiently and cost-effectively they can run their operations, and make changes that will improve patient outcomes.
- Track patient location, equipment
- Improve staff deployments in real-time
- Optimize the use of operating rooms
[Try Demo](/products/demos/)
#### An Easy Decision to Make
Getting started with Row64 is fast and cost-effective.
##### Quickly understand your data investment
Clear ROI with simple, transparent licensing based on your computing requirements.
##### No need to change your infrastructure
Row64's flexible technology does not require overhauls of your data infrastructure or workflows.
##### Reduce the burden on your data team
Accessible interface makes it easy for anyone - not just data scientists - to serve themselves.
##### Easily scale to meet your needs
Parallel compute and other optimizations sustain platform performance as you scale.
#### Proud Partners
We're proud to collaborate with some of the biggest and best players in the game. Here are a few of the companies we work with.
[](https://www.carahsoft.com/row64?utm_source=row64&utm_medium=referral&utm_campaign=row64&utm_content=partner-presence)
> "Row64 lets us deliver near real-time, event-driven insights to our customers instead of overnight batch data — and that transparency changes how they operate. We get them to market faster, and the modular platform grows as they do."
>
> 
>
> Mike Klinefelter VP of Global Technology, Northern Lights Technology
### Every asset, live. Act while it matters
Row64 — a real-time operational platform for assets in motion: high-velocity data streams become one live command center, and critical events become response in seconds.
[See it live](/products/demos/) [How it works](/products/)
Supply Chain
Fleet Management
Utilities
Smart Cities
#### Your whole operation, on one screen
Zoom from the world to a single site. Cross-filter live telemetry to see what’s happening, where, and why — and act the moment it happens. Three things make that possible:
##### More than a data pipeline. A real-time engine
Analytics pipelines land data in a warehouse and wait for the next refresh. Row64 aggregates live streams in memory as they arrive. One current state, ready for action.
Streaming Video
[Inside the engine](/products/speed/)
##### More than a static dashboard. A live actionboard
Even a live dashboard only reports what happened. A live actionboard composes maps, streaming data, warehouse data, diagrams, and video into one command center — every point at full resolution, no sampling, no clustering.
GPU Tiler Video
[Explore the actionboard](/products/visual/)
##### More than an alert. Action in real time
An alert still waits for someone to read it. Row64 turns detection into response the moment a condition trips — from the actionboard, or fully headless through the APIs. No screen required.
Actions Video
[How automation works](/products/extensible/)
#### Every operation has a Detection Gap
Fleet Dispatch
The stall flags within seconds of the telemetry arriving — rerouted before the delay compounds.
A three-hour discovery became a thirty-second one.
Supply Chain & Logistics
The yard lead gets the threshold alert while the trailer is still in the yard.
A cost you reported became a cost you prevented.
Field Operations
The breach renders as it happens, with the asset's live context around it.
The operator acts inside the event, not after it.
[Explore use cases by industry](/solutions/)
> "Most supply chains run on historical visibility — dashboards reviewed after the fact, decisions made too late. Row64 transforms data into action: identifying risks, prioritizing responses, and enabling faster decisions before disruptions impact operations."
>
> 
>
> John Hantzis CEO & Founder, JDH Logistics
#### Go live in days, not quarters
Here's the path
1
Connect your sources
We connect your live and batch sources: Kafka, MQTT, devices, and warehouse feeds. No rip and replace.
2
Stand up your first live view
Your operation, on screen, against real events — typically within days.
3
Wire conditions that matter
Your operators define the detections and actions: a geofence exit, a dwell threshold, a missed check-in.
4
Evaluate on your data
Prove it against your real operation, not a canned demo. Get there in a few weeks.
[Let's review your environment](/company/see-a-demo/)
## Products
### The Row64 Stack
Row64 optimizes for decision speed with a GPU-accelerated, vertically integrated stack that scales in volume, throughput, and number of clients.
Platform Overview
A vertically integrated runtime — ingest (stream or batch), in-memory data store, high-speed compute, GPU-rendered interactive visualizations, and a full API surface for automation — all sharing a open quickaccess binary format.
Stream
Stream aggregation and processing coming from streaming services (e.g Kafka), brokers (e.g. MQTT) — with full Python streaming API
RAMDB
An in-memory data store that holds the current state data in one place: live telemetry (streaming data), geographic layers (maps), warehouse data (structured), and files like images and PDFs (unstructured) content.
Server
A high-speed WebSocket compute server orchestrating bidirectional communication of streaming events to and from clients
GPU Dashboards
Browser-based, GPU-accelerated dashboards render interactive visuals in real time combining telemetry, geographic layers, warehouse data, and unstructured content across a wide range of devices.
Studio
An intuitive authoring environment usable by operators and engineers alike where dashboards and data models are built.
APIs
Every capability is API accessible: automate ingest, stream processing, creating and modifying dashboards, and triggering actions from events — or run fully headless: events in, actions out, no screen required.
#### Row64's Three Pillars
What sets Row64 apart is its real-time performance, its unified visual layer, and its end-to-end automation.
#### Real-Time Performance Everywhere
Row64's runtime eliminates latency. From ingesting sub-millisecond data streams to high-speed GPU compute and rendering of massive datasets, operators see and react as events unfold, not on batch refresh cycles. High-volume, continuously changing telemetry processes with sub-second latency.
[Explore realtime compute](/products/speed/)
#### Every Asset. One Unified Canvas
A GPU-rendered surface — with an integrated GPU vector tiler, built for data, not cartography — combines real-time telemetry, unlimited geographic layers, warehouse data, and unstructured content, enabling operators to visualize, cross-filter, and interact instantly with a complete picture—and only the signals that matter— without switching tools.
[Explore realtime visualization](/products/visual/)
#### From Detection to Action Automatically
Every surface is programmable. Row64's open, API-first architecture makes detection, understanding, and action callable, so events automatically trigger action when needed. Every response can integrate with streaming brokers, agents, and action systems — no replacement of existing infrastructure required.
[Explore APIs](/products/extensible/)
#### Up and Running in Days, Not Quarters
#### More Compute Without More Cost
Row64's stack delivers greater computing power and performance with lower-cost infrastructure.
#### Run in Any Environment
On prem, in the cloud, or on the edge, Row64's highly performant, real-time architecture runs in any environment or operating system.
#### Adapt to Any Workflow
Operators interact with the visual canvas; engineers automate via APIs — purpose-built surfaces for each role: automation or human in the loop
#### Security at Scale
Federated security, layered access controls, and SELinux support, enterprise-grade protection without compromising operational speed.
#### Technology Partners
We're proud to collaborate with some of the biggest and best players in the game. Here are a few of the companies we work with.
### One Canvas. Multiple Data Types. All Live
Row64’s browser-based GPU-accelerated canvas brings live telemetry, geographic layers, warehouse data, and unstructured content together, so operators can see, filter, and act on what’s happening, without switching tools.
#### GPU-Rendered. WebAssembly-Powered. Built for Scale
Row64's canvas compiles to WebAssembly and executes rendering directly on the client GPU — through Row64's GPU vector tiler, built for data, not cartography — delivering millisecond-precision frame updates, pixel-level selection accuracy, and zero-latency cross-filtering regardless of data volume or update frequency.
#### A GPU vector tiler built for data, not cartography
Conventional map stacks tile geography and decorate it with a thinned data layer. Row64 inverts that: a GPU vector tiler designed for the data itself, rendering full-resolution telemetry, geographic layers, and operational context as one surface. The result: a live spatial understanding of how everything is changing.
#### GPU Accelerated Draw & Selection
Interact with live data at pixel-level precision. Select, double-click, or drill into points, lines, shapes, images, and pop-ups — all rendered at GPU speed for instant response.
#### Unstructured Content
Bring PDFs, CAD drawings, video, 3D files, and design documents directly into your operational canvas — alongside live data, not in a separate window.
#### Deep Cross Filtering
Filter across every data type simultaneously. Row64 automatically reveals complex relationships between warehouse and unstructured content — no manual setup, no waiting for queries to run.
#### Powerful Formula and Recipe System
Transform, clean, and analyze operational data directly in the canvas. High-speed regular expressions, fuzzy search, sentiment analysis, and data transformation tools — all in easy-to-use A1 notation.
#### More Ways to Interact
Every operator works differently. Row64's GPU Dashboards offers multiple customizable options for interacting and visualizing data.
#### High-Quality Business Charts
All new levels of detail and precision for favorite chart types like pie, bar, line, trellis, scatter, etc.
#### Nodes and Dense Graphs
Visualize complex operational relationships, org structures, and process flows at the scale of your entire organization.
#### Markers
Precise map markers and sprites give operators instant geographic points of reference across highly detailed operational maps.
#### Dataframes & Spreadsheets
Integrated dataframe & spreadsheet views with A1 notation for complex calculations — embedded directly in the canvas, not in a separate tool.
#### Technology Partners
We're proud to collaborate with some of the biggest and best players in the game. Here are a few of the companies we work with.
### Built to Automate at Every Step
Every capability in Row64's operational runtime is API-accessible — so teams can automate from signal to decision, without replacing existing infrastructure.
#### One Runtime. Fully Programmable
Ingest and transform data, automate visual output, connect live streams, embed custom interfaces, and deploy dashboards programmatically — or run fully headless. Events in, actions out, through the Stream and Data APIs: if your workload is automated end to end, the command center is optional; the runtime isn't.
#### Data APIs
Data flow in Row64 centers on high-speed, low-level byte data moving between applications and services. The Data API makes it simple to load, read, and transform data using the familiar Python syntax.
[See Documentation](https://app.row64.com/Help/V3_5/API_Docs/row64tools/row64tools_Usage/)
#### Draw APIs
This Python scripting language generates maps and diagrams. It handles fast-changing visual data and can be used for overlaying site and city planning or generating large-scale organizational charts and business process maps.
Coming Soon
#### Stream APIs
The Stream API provides flexible connections to data streaming services, including MQTT, Kafka, WebSockets, and databases. With it, users can detect alerts, events, and incidents, and orchestrate responses through data, interfaces, or events.
Coming Soon
#### Web APIs
The Web API exposes JavaScript functions that allow users to design their own UI experiences and workflows. By manipulating JavaScript functions and events via the API, users can add or edit dashboard functionality, create pop-ups that drill into data, customize dashboard workflows, and more.
[See Documentation](https://app.row64.com/Help/V3_5/API_Docs/JavaScript/)
#### Dash API
The Dash API allows users to automate dashboard creation, deployment, and modification. Dashboard patterns can be engineered as a modular framework that dynamically creates the right dashboard for the right user. A master dashboard can be individualized for multiple clients, properties, offices, or franchises.
[See Documentation](https://app.row64.com/Help/V3_5/API_Docs/DashAPI_Docs/)
#### No Rip and Replace
Row64 offers flexible deployment patterns tailored to your infrastructure and operational requirements. Common patterns include:
Existing RT Pipelines
Row64 Streaming Server receives data from your existing pipeline and aggregates it into RAMDB.
Real-Time Device Direct
Events from devices stream directly into RAMDB. Operators and action systems receive very low latency updates.
RT and Batch Fusion
Simultaneously ingest live telemetry and batch updates from warehouse systems on a defined cadence. Both feeds are written to RAMDB.
#### Technology Partners
We're proud to collaborate with some of the biggest and best players in the game. Here are a few of the companies we work with.
### Operational Intelligence Examples
Choose from the list below to interact with our live examples.
- Telecommunications See your geospatial data come alive. Interact in real time with a map containing over 2 million cell towers displayed as geo-located data points. Apply custom shapes or filters on multi-million row datasets, and see the entire dashboard update with sub-second response times. [Start Demo](javascript:void(0);) To toggle fullscreen press the near the upper right corner of the frame. Before starting, please familiarize yourself with the list of things to try. Instructions 1. Zoom in and out (using mouse or trackpad) on the map. 2. Click the **Selection** icon . In the second drop-down, pick **Shape Mask**. 3. Move the mouse over the map, and click on a shape to select cell towers in that country. 4. Click the blue at the top to clear filters. 5. Change the **Shape Mask** to any other selection back in the **Selection** menu. 6. Hold the **Space** key and drag the mouse over the map to select cell towers.
- Power Lines The dashboard displays a powerline map with reported incidents, built using custom geo shapes and multiple geo layers. Ensure grid availability by combining custom geo layers, interactive filtering, and large-scale data responsiveness on a single map. Instantly explore powerline infrastructure and incidents by voltage level or region—without the limitations of traditional mapping or third-party tools. [Start Demo](javascript:void(0);) To toggle fullscreen press the near the upper right corner of the frame. Before starting, please familiarize yourself with the list of things to try. Instructions 1. Click the **Map Menu** in the top-left corner of the map. 2. Select **Layer Menu** to toggle between map layers. 3. In the **Map Menu**, select **Lasso** from the second drop-down menu. 4. Hold the **Space** key and draw a lasso around an area to filter data. The detailed records tables on the left update instantly. 5. Click the blue at the top to clear the filters. 6. Move the slider filter at the bottom to filter out power lines on the map by different voltage levels, and see the map update in real-time.
- Grid Operations See how Row64 creates your grid reliability ops center in a single unified view that drives action with your downstream systems.
- Water Systems Explore the data displaying water systems in the state of Kentucky. Seamlessly overlay multiple map layers and draw custom geographies to gain lightning-fast insights about water systems that transform raw information into actionable decisions. [Start Demo](javascript:void(0);) To toggle fullscreen press the near the upper right corner of the frame. Before starting, please familiarize yourself with the list of things to try. Instructions 1. Click on the **Map Menu** icon in the top-left corner. 2. In the second drop-down, select **Circle** or **Rectangle**. 3. Holding the **Space** key, drag the mouse to draw a rectangle over the map. 4. See water lines update in real-time. 5. Click the blue at the top of the window to clear the filters. 6. Click a bar in the bottom-right chart to filter the data by decade. 7. Move the slider to select the served population.
- Fleet and Logistics Track fleet movement on a detailed map trigger alerts and communicate with downstream systems.
- Intelligent Cities Explore the data on fire hydrant repair and maintenance by transforming complex infrastructure data into an intuitive, interactive experience. Combine pixel-perfect maps, documents, images, and even 3D models in a single high-performance visualization for faster, more informed decisions. Visualize every layer of your data in detail! [Start Demo](javascript:void(0);) To toggle fullscreen press the near the upper right corner of the frame. Before starting, please familiarize yourself with the list of things to try. Instructions 1. Zoom in and out with the mouse to navigate the map, viewing locations of all city fire hydrants. 2. Click on the **Map Menu** icon in the top left corner, then click on the **Map Layers** icon . Click the check boxes to toggle layers on the map. 3. Double-click a hydrant marker on the map to pop up unit details, including images. 4. Click on the image to enlarge it. 5. Double-click a **PDF** icon on the map for detailed unit information. Click on the **PDF** icon to open the unit diagram specs. 6. Double-click the **3D** icon on the map, then click on the **GLB** icon in the pop-up to interact with a 3D model. 7. Use the mouse wheel or trackpad to zoom and rotate the model for a closer inspection.
- Urban Analytics Analyze complex city planning initiatives in real time. Combine multi-layer geospatial maps with structured and unstructured data. Instantly filter, select, and explore spatial areas, then access associated files, such as DWG, from the dashboard. [Start Demo](javascript:void(0);) To toggle fullscreen press the near the upper right corner of the frame. Before starting, please familiarize yourself with the list of things to try. Instructions 1. Click on the **Selection Menu** icon in the top-left corner of the map. 2. Click on the **Shape Layer** icon in the menu. 3. Uncheck the **Inflow** and **Central Core** layers. 4. On the map, select the **MP4** and **DWG** icons with a single mouse click. You can see the rest of the data has been filtered accordingly. 5. In the details table on the right, double-click the **DWG** and **MP4** icons to open the files and see design details and videos.
- Retail Price Optimization Row64 transforms millions of fragmented POS, inventory, and product records into a unified, real-time operational intelligence hub for store and category managers. This interface allows users to explore the interactive floor plan, simulate price changes, and act on data-driven recommendations without the limitations of traditional BI tools or manual spreadsheets. [Start Demo](javascript:void(0);) To toggle fullscreen press the near the upper right corner of the frame. Before starting, please familiarize yourself with the list of things to try. Instructions 1. Explore the dynamic floor layout by zooming in and out on the diagram. Darker-colored shelves have a higher potential to increase revenue. 2. Double-click on any shelf for additional details, such as profit, cost, and margin. 3. View the prescriptive insights section to view SKU level price recommendations. 4. Adjust the Price Increase or Decrease sliders to verify the Simulated **Price** that results in the maximum Simulated **Revenue**. 5. Select store #5 in the filter section in the top left. 6. Hold the **Space** key. Drag the mouse over the floor diagram to highlight one or more shelves and filter the resulting data. 7. Notice the Prescriptive Insight section is updated and recalculated dynamically and in real-time.
- Credit Card Fraud Detection Visually explore financial records from multiple sources across North America. See how the real-time dashboards update as transactions stream in from credit cards across different regions. Drill into specific transactions by record or location, enabling fraud detection teams to spot emerging threat patterns as they happen, not after the fact. [Contact Us](/company/contact-us/) for access to a live credit card fraud detection streaming example on our servers.
- Retail Transform visualizations into fully interactive experiences. The platform allows users to combine custom geomarkers, layered maps, rich media, and instant data filtering to explore billions of sales records visually and intuitively. [Start Demo](javascript:void(0);) To toggle fullscreen press the near the upper right corner of the frame. Before starting, please familiarize yourself with the list of things to try. Instructions 1. Click the **Map Menu** icon in the top-left corner of the map, then select **Layer Menu** to toggle between map layers. 2. Hold the **Space** key and draw a shape around an area to filter data. The detailed records table updates instantly. 3. In the table, drag the mouse while holding the left button to highlight the first five row indices. 4. Double-click on a shoe image to make it larger. Right-click on the image to save it locally.
- Profit Analysis Use Row64's Spreadsheet module to bring together live spreadsheet-style calculations at the scale of billions of records with advanced geospatial analytics. Row64 delivers a single, interactive dashboard where you can layer insights, filter them dynamically, and make decisions in real-time. [Start Demo](javascript:void(0);) To toggle fullscreen press the near the upper right corner of the frame. Before starting, please familiarize yourself with the list of things to try. Instructions 1. Select data cohorts directly from the bar charts to filter and refine insights. 2. Change the selections in the filter at the top or select cohorts directly from the bar charts. 3. [Learn more about how spreadsheets work in the Row64 platform](/training/expenses/).
- Sentiment Analysis Deliver instant, lag-free data exploration of sentiment with visual updates that appear immediately as you calculate, filter, and drill into even the largest datasets. [Start Demo](javascript:void(0);) To toggle fullscreen press the near the upper right corner of the frame. Before starting, please familiarize yourself with the list of things to try. Instructions 1. Modify the dates in the **Date Range** filter in the top-left corner to span 2015 to 2019. 2. Click on the **Filter** icon in the keywords table at the bottom left, and type ***\*computing*** for a wildcard search. 3. Press the **F4** key on the keyboard to toggle live update mode. 4. Adjust any slider for Min or Max sentiment to see real-time word cloud updates.
- Business Processes Inspect the business process diagram on the dashboards by turning custom diagrams, CAD drawings, and blueprints into fully interactive elements of your data visualization layer. Seamlessly combine structured and unstructured data to deliver massive amounts of information to decision-makers. [Start Demo](javascript:void(0);) To toggle fullscreen press the near the upper right corner of the frame. Before starting, please familiarize yourself with the list of things to try. Instructions 1. Zoom in and out using your mouse wheel or trackpad. 2. Click on **Package Order** and **Ship Order** stages of the process on the diagram to filter the entire dashboard by those steps. 3. Click the blue at the top to clear filters. 4. Click on the **Map Menu** icon at the top-left corner of the diagram. 5. Select **Rectangle** from the second drop-down. 6. Hold the **Space** key and drag the mouse over the process diagram to highlight multiple steps in the process.
- Organization Charts Transform static org charts, diagrams, CAD drawings, and more into dynamic, data-driven visualizations. With multi-layered employee data—such as skills, salary, and performance—directly linked to the dashboard, you can filter, zoom, and drill down in real time and deliver dynamic, low-latency, and custom visualization solutions to stakeholders. [Start Demo](javascript:void(0);) To toggle fullscreen press the near the upper right corner of the frame. Before starting, please familiarize yourself with the list of things to try. Instructions 1. Use the drop-down in the top right corner to center the diagram on a specific department. 2. Adjust the sliders at the bottom right to filter employees by their skill level in programming or data viz, and watch the org chart update in real time. 3. Click the blue at the top of the screen to clear filters. 4. Hold the **Space** key and drag the mouse to draw a rectangle over the org chart. 5. Notice that the data is filtered out immediately. 6. Click on individual boxes to filter individual data.
- AI Observability Visually explore high-volume AI agent telemetry across complex workflows. See how Row64 real-time visualizations update as prompts, tool calls, and model decisions stream in from multiple agents. Drill into individual traces by step or agent, enabling teams to spot emerging risk patterns, failures, and anomalies as they happen, not after the fact. [Contact Us](/company/contact-us/) for access to a live AI Observability streaming example on our servers.
- Cyber Security Live stream data updates of a cyber attack in progress allowing you explore data without pre-aggregation or sampling—no manual refresh required. The top table displays the most granular records, updated in real time, empowering security engineers and analysts to act immediately as events unfold. [Contact Us](/company/contact-us/) for access to a live cybersecurity streaming example on our servers.
Fleet and Logistics
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### Pricing
#### Predictable Pricing
Row64 offers pricing that includes all core platform capabilities: connectors, streaming data, analytics, interactive visualization, large data sets, and either on-prem, cloud, or hybrid deployment.
##### Faster ROI over building from scratch
Achieve clear ROI with straightforward licensing tailored to your computing requirements.
##### Transparent monthly cost
Server-based licensing assures you’re never surprised by changing monthly bills as you grow.
##### Scalable to larger teams and workflows
Able to quickly scale up to meet your growing needs without additional costs.
#### Customized Workflows
Row64 can provide tailored workflows based on specific needs, such as streaming device-specific data or map types, performance tuning based on requirements, and more.
[Contact Us](/company/contact-us/)
### No Latency. No Limits
Unlike fragmented stacks, Row64's components share one format. There's no overhead from transforming or copying data between layers — which is why it runs up to 100x faster on high-volume workloads.
#### Up to 100x Faster on High-Volume Workloads
Row64 eliminates platform-induced latency from data ingestion through computation to visualization. Speed never degrades as data volume increases.
#### Parallel Processing
Every computation runs on its own dedicated thread simultaneously, so whether multiple users, panes, or computations, there is never any waiting.
#### GPU-Accelerated Compute
Compute resources are dynamically load-balanced across CPU and GPU, so the full power of your hardware is always working for your operators, not sitting idle.
#### Contiguous Memory
Row64 stores data in a contiguous open bytestream format, so reads stay cache-friendly and throughput stays predictable as data volume grows.
#### Fast Unstructured Data Processing
Text, paragraph, and unstructured content — historically the biggest bottleneck in operational dashboards — are automatically threaded and optimized. No configuration required.
#### Additional Optimizations
#### Hybrid Engine
Intelligently routes compute across CPU and GPU based on workload type — so every operation runs at peak efficiency.
#### Threaded Compression
Compresses data across multiple processors simultaneously, accelerating transfer without sacrificing fidelity.
#### Zero Copy Communications
Data moves between components by reference rather than being copied, decreasing latency on high-volume workloads.
#### Caching Strategies
Contiguous memory, by design. When data sits adjacent in RAM, the prefetcher pulls the next block before the CPU asks — spatial locality that keeps cache hits high and stalls low
#### Technology Partners
We're proud to collaborate with some of the biggest and best players in the game. Here are a few of the companies we work with.