Apify to Databricks SQL Warehouse Integration

How Datrise loads Apify into Databricks SQL Warehouse

Datrise syncs Apify's actors, runs, datasets, key-value stores, request queues, and run logs 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

Apify: Web scraping and browser automation platform.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Apify entities map to Databricks SQL Warehouse

Apify entity	Databricks SQL Warehouse object	Notes
actors	`apify_actors`	id PK · custom fields → VARIANT or STRUCT columns
runs	`apify_runs`	id PK · linked to apify_actors
datasets	`apify_datasets`	id PK · linked to apify_actors
key-value stores	`apify_key_value_stores`	id PK · linked to apify_actors

FAQ

How does Datrise handle Apify'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 Apify 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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