Apify to Redash Integration
AI-first ETL from Apify into Redash. Governed entities, incremental sync, typed landing tables.
How Datrise loads Apify into Redash
Datrise syncs Apify's actors, runs, datasets, key-value stores, request queues, and run logs into Redash as SQL tables Redash queries and visualizes. Flexible or custom fields land in flattened columns for query results, and timestamps such as created, updated, and status changes are typed as temporal columns.
Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts for scheduled queries. Redash caches query results on a schedule, so Datrise keeps tables incrementally fresh so cached dashboards reflect reality.
Ideal for lightweight, query-driven dashboards.
Endpoints
Apify: Web scraping and browser automation platform.
Redash: Open-source SQL client for queries, visualizations, and dashboards.
How Apify entities map to Redash
| Apify entity | Redash object | Notes |
|---|---|---|
| actors | apify_actors | id PK · custom fields → flattened columns for query results |
| 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 Redash?
Flexible values are stored as flattened columns for query results, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Redash types.
How does the Apify to Redash sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
Related pipelines
More destinations for Apify
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