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Pipeline Complexity is Overwhelming

⚠️ Streaming logic spreads across too many tools

⚠️ APIs are bolted on after-the-fact

⚠️ Dev cycles get lost in plumbing, not product

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Simple SQL Underneath

DataSQRL was built with DATA in mind, not code.

When you want to dig deeper into your pipeline, SQL is all you need.

DataSQRL keeps the entire pipeline IN SYNC, while you connect your data to your AI.

See DataSQRL in Action

DataSQRL supports both real-time and scheduled workloads using the same declarative model.
No switching contexts. No rewriting logic. No surprises.

Defining Data Interfaces with FlinkSQL

Blogs + News

DataSQRL 0.6 Release
The Streaming Data Framework

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Why Temporal Join is Stream Processing’s Superpower

Use natural language to build

Use natural language to describe your requirements.

DataSQRL automates the pipeline build.

Then you can talk directly to your data, like you do with ChatGPT.

Fix Your Fractured Data

Real-Time Isn’t Just a Speed Problem — It’s a Stack Problem

DataSQRL Step-By-Step

DataSQRL Compiler

DataSQRL compiles SQL to an integrated data pipeline that runs on mature open-source technologies.

Deploy with Docker, Kubernetes, or cloud-managed services.

Integrations

We integrate with what you already use

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Built for Data Architects Who Build Like Engineers

SQL + GraphQL = Data Product
Define what you want. Let DataSQRL handle how it runs.
Infra as Output
Compiled pipelines. Docker-ready. Kubernetes-native.
Streaming-First, But Not Streaming-Only
Designed for Kafka and Flink. Optimized for Postgres. Works anywhere.

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