DatriseAI-first ETL

CircleCI to Databricks SQL Warehouse Integration

AI-first ETL from CircleCI into Databricks SQL Warehouse. Governed entities, incremental sync, typed landing tables.

How Datrise loads CircleCI into Databricks SQL Warehouse

Datrise syncs CircleCI's pipelines, workflows, jobs, builds, test results, and artifacts into Databricks SQL Warehouse as a Delta Lake table per source entity. Flexible or custom fields land in VARIANT or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses a Delta MERGE on stable id, with change history available via time travel, so re-runs update only what changed. Delta partitioning by load date with OPTIMIZE/Z-ORDER on query keys. Datrise writes Unity Catalog–governed Delta tables, so lineage and permissions are managed centrally rather than per-notebook.

Ideal for lakehouse analytics and ML feature tables on Databricks.

Endpoints

CircleCI: Continuous integration and delivery pipelines.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How CircleCI entities map to Databricks SQL Warehouse

CircleCI entityDatabricks SQL Warehouse objectNotes
pipelinescircle_ci_pipelinesid PK · custom fields → VARIANT or STRUCT columns
workflowscircle_ci_workflowsid PK · linked to circle_ci_pipelines
jobscircle_ci_jobsid PK · linked to circle_ci_pipelines
buildscircle_ci_buildsid PK · linked to circle_ci_pipelines

FAQ

How does Datrise handle CircleCI's custom fields in Databricks SQL Warehouse?

Flexible values are stored as VARIANT or STRUCT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Databricks SQL Warehouse types.

How does the CircleCI to Databricks SQL Warehouse sync stay up to date?

It runs incrementally — Datrise uses a Delta MERGE on stable id, with change history available via time travel.

Related pipelines

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