Operating System for Data™
Build GenAI applications, data products, and data-driven features quickly at scale without a PhD in data systems.
With DataSQRL = Build Quickly
Without DataSQRL = Data Plumbing Nightmare
How DataSQRL Works
Implement your data processing in SQL and define your data API in GraphQL.
DataSQRL compiles optimized data pipelines that are robust, scalable, and easy to maintain.
Use Cases
Data Mesh
DataSQRL empowers domain teams to develop streaming data products autonomously. Build a self-service data platform with existing skills.
Event-Driven Microservices
Efficiently process events in realtime and expose the results through consumable APIs in an event-driven architecture.
Observability & Automation
Build tailored observability platforms that turn your metrics into insights. Automate your processes with custom rules and AIOps.
Why DataSQRL?
To compete, you need to unlock the value of your data by building data products. But assembling complex technologies into bespoke data pipelines wastes most of your time on data plumbing, which causes 80% of data products to fail. DataSQRL eliminates data plumbing so you can focus on enriching your data to deliver value efficiently.
How DataSQRL Helps Your Team
Saves You Time
DataSQRL allows you to focus on your data processing by eliminating the data plumbing that strangles your data pipeline implementation with busywork: data mapping, schema management, data modeling, error handling, data serving, API generation, and so on.
Easy to Use
Implement your data processing with the SQL you already know. DataSQRL allows you to focus on the "what" and worry less about the "how". Import your functions when SQL is not enough - DataSQRL makes custom code integration easy.
Fast & Efficient
DataSQRL builds efficient data pipelines that optimize data processing, partitioning, index selection, view materialization, denormalization, and scalability. There actually is some neat technology behind this buzzword bingo.
Fully Customizable
Open Source
