DatriseAI-first ETL

Heroku Databricks SQL Warehouse

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

How Datrise loads Heroku into Databricks SQL Warehouse

Datrise syncs Heroku'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

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

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Heroku entities map to Databricks SQL Warehouse

Heroku entityDatabricks SQL Warehouse objectNotes
recordsheroku_recordsid PK · custom fields → VARIANT or STRUCT columns
eventsheroku_eventsTIMESTAMP events
configuration objectsheroku_configuration_objectsid PK · linked to heroku_records

FAQ

How does Datrise handle Heroku'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 Heroku 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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