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DataSQRL Examples

These examples demonstrate how AI coding agents use DataSQRL to build production-grade data products from data catalog definitions. In practical implementations, an autonomous data platform maintains a central data catalog that agents reference to build data products, data pipelines, and data APIs.

Commerce Example​

A retail commerce scenario with customer orders, products, and inventory data.

  • Data Catalog: Defines the data sources, schemas, and connectors for the commerce domain
  • Sample Data Products: AI-generated data pipelines and APIs built from the catalog

Finance Example​

A banking scenario with accounts, transactions, and customer data.

  • Data Catalog: Defines the data sources, schemas, and connectors for the finance domain
  • Sample Data Products: AI-generated data pipelines and APIs built from the catalog

How It Works​

Each example follows the same pattern:

  1. Data Catalog: Defines available data sources with schemas, connectors, and sample data for testing
  2. Agent Implementation: A coding agent uses the catalog to build data products, iterating with DataSQRL's test command until tests pass
  3. Deployment Artifacts: DataSQRL compiles the SQRL scripts into production-ready Flink plans, Kafka topics, Postgres schemas, and GraphQL APIs

These examples show how DataSQRL's feedback loop guides agents toward correct, production-grade implementations.