Kestra Recipes¶

Run A Quality Check In A Python Script Task¶

Use check_quality_script when the flow already has a resolved data URI and only needs a focused quality gate before the next task.

This is the simplest production pattern for Kestra-backed validation.

Stream Validation For Large Inputs¶

Use stream_quality_script when the data source is large enough that you want chunked or streaming-style validation rather than one eager in-memory pass.

Generate A Standard Team Template¶

Use generate_check_flow or generate_quality_pipeline when multiple teams need the same basic quality-flow skeleton with only namespace, dataset URI, or rule differences.

Emit Structured Outputs For Downstream Tasks¶

Use the output helpers when downstream Kestra tasks need a normalized payload rather than scraping console logs.

Add SLA Hooks For Production Pipelines¶

Kestra is often the place where operator-facing alerts matter most. Pair script execution with SLA monitoring and notification hooks so production incidents are routed consistently.