DatriseAI-first ETL

Autopilot Activities Databricks SQL Warehouse

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

How Datrise loads Autopilot Activities into Databricks SQL Warehouse

Datrise syncs Autopilot Activities's records, events, and configuration objects 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

Autopilot Activities: SaaS or API data source for analytics and warehouse sync.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Autopilot Activities entities map to Databricks SQL Warehouse

Autopilot Activities entityDatabricks SQL Warehouse objectNotes
recordsautopilot_activities_recordsid PK · custom fields → VARIANT or STRUCT columns
eventsautopilot_activities_eventsTIMESTAMP events
configuration objectsautopilot_activities_configuration_objectsid PK · linked to autopilot_activities_records

FAQ

How does Datrise handle Autopilot Activities'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 Autopilot Activities 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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