Apify to Birst Integration
AI-first ETL from Apify into Birst. Governed entities, incremental sync, typed landing tables.
How Datrise loads Apify into Birst
Datrise syncs Apify's actors, runs, datasets, key-value stores, request queues, and run logs into Birst as warehouse tables for Birst's automated star schema. Flexible or custom fields land in flattened columns, and timestamps such as created, updated, and status changes are typed as date/time dimensions.
Sync is incremental: Datrise uses incremental refresh of the source tables Birst ingests, so re-runs update only what changed. Date-partitioned facts. Birst builds its own semantic layer, so Datrise lands conformed, well-keyed tables it can automate against.
Ideal for networked, governed enterprise BI.
Endpoints
Apify: Web scraping and browser automation platform.
Birst: Cloud BI with networked analytics and enterprise semantic layers.
How Apify entities map to Birst
| Apify entity | Birst object | Notes |
|---|---|---|
| actors | apify_actors | id PK · custom fields → flattened 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 Birst?
Flexible values are stored as flattened columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Birst types.
How does the Apify to Birst sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the source tables Birst ingests.
Related pipelines
More destinations for Apify
Early access
Connect Apify to Birst the easy way
Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.