DatriseAI-first ETL

Impact to Databricks SQL Warehouse Integration

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

How Datrise loads Impact into Databricks SQL Warehouse

Datrise syncs Impact's partners, actions, clicks, conversions, payouts, and campaigns 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

Impact: Partnership and affiliate performance tracking.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Impact entities map to Databricks SQL Warehouse

Impact entityDatabricks SQL Warehouse objectNotes
partnersimpact_partnersid PK · custom fields → VARIANT or STRUCT columns
actionsimpact_actionsid PK · linked to impact_partners
clicksimpact_clicksid PK · linked to impact_partners
conversionsimpact_conversionsid PK · linked to impact_partners

FAQ

How does Datrise handle Impact'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 Impact 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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