Apify to Looker Integration
AI-first ETL from Apify into Looker. Governed entities, incremental sync, typed landing tables.
How Datrise loads Apify into Looker
Datrise syncs Apify's actors, runs, datasets, key-value stores, request queues, and run logs into Looker as governed warehouse tables with LookML-ready naming. Flexible or custom fields land in flattened columns (nested fields expanded for modeling), and timestamps such as created, updated, and status changes are typed as date/time dimension columns.
Sync is incremental: Datrise uses incremental refresh of the underlying warehouse tables Looker explores, so re-runs update only what changed. Date-partitioned fact tables for PDT performance. Looker models live in LookML on top of SQL, so Datrise lands clean, stable column names rather than churn that would break your views.
Ideal for governed, version-controlled BI on a warehouse.
Endpoints
Apify: Web scraping and browser automation platform.
Looker: Google Cloud BI with LookML semantic models and governed dashboards.
How Apify entities map to Looker
| Apify entity | Looker object | Notes |
|---|---|---|
| actors | apify_actors | id PK · custom fields → flattened columns (nested fields expanded for modeling) |
| 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 Looker?
Flexible values are stored as flattened columns (nested fields expanded for modeling), so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Looker types.
How does the Apify to Looker sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the underlying warehouse tables Looker explores.
Related pipelines
More destinations for Apify
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