Apify to Yellowfin Integration
AI-first ETL from Apify into Yellowfin. Governed entities, incremental sync, typed landing tables.
How Datrise loads Apify into Yellowfin
Datrise syncs Apify's actors, runs, datasets, key-value stores, request queues, and run logs into Yellowfin as warehouse tables Yellowfin builds views on. 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 connected tables, so re-runs update only what changed. Date-partitioned facts. Yellowfin views reference columns by name, so Datrise lands stable, well-typed columns to keep reports valid.
Ideal for dashboards with automated data storytelling.
Endpoints
Apify: Web scraping and browser automation platform.
Yellowfin: BI suite with dashboards, automated insights, and data storytelling.
How Apify entities map to Yellowfin
| Apify entity | Yellowfin 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 Yellowfin?
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 Yellowfin types.
How does the Apify to Yellowfin sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
Related pipelines
More destinations for Apify
More sources for Yellowfin
Early access
Connect Apify to Yellowfin the easy way
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