DatriseAI-first ETL

Import API to Databricks SQL Warehouse Integration

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

How Datrise loads Import API into Databricks SQL Warehouse

Datrise syncs Import API's records, payloads, batches, schemas, ingest timestamps, and source keys 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

Import API: Direct API push of custom records into a pipeline.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Import API entities map to Databricks SQL Warehouse

Import API entityDatabricks SQL Warehouse objectNotes
recordsimport_api_recordsid PK · custom fields → VARIANT or STRUCT columns
payloadsimport_api_payloadsid PK · linked to import_api_records
batchesimport_api_batchesid PK · linked to import_api_records
schemasimport_api_schemasid PK · linked to import_api_records

FAQ

How does Datrise handle Import API'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 Import API 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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