Apify to CSV Files Integration
AI-first ETL from Apify into CSV Files. Governed entities, incremental sync, typed landing tables.
How Datrise loads Apify into CSV Files
Datrise syncs Apify's actors, runs, datasets, key-value stores, request queues, and run logs into CSV Files as one CSV per source entity. Flexible or custom fields land in JSON-encoded strings for nested fields, and timestamps such as created, updated, and status changes are typed as ISO-8601 timestamp columns.
Sync is incremental: Datrise uses writes a fresh, fully-typed CSV per entity each run, so re-runs update only what changed. Optional date-suffixed files for change tracking. CSV has no types, so Datrise emits a companion schema and quotes/escapes consistently so downstream loaders don't misparse commas and newlines.
Ideal for portable hand-off into any tool that ingests delimited files.
Endpoints
Apify: Web scraping and browser automation platform.
CSV Files: Flat-file destination for exports and lightweight data sharing.
How Apify entities map to CSV Files
| Apify entity | CSV Files object | Notes |
|---|---|---|
| actors | apify_actors | id PK · custom fields → JSON-encoded strings for nested fields |
| 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 CSV Files?
Flexible values are stored as JSON-encoded strings for nested fields, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native CSV Files types.
How does the Apify to CSV Files sync stay up to date?
It runs incrementally — Datrise uses writes a fresh, fully-typed CSV per entity each run.
Related pipelines
More destinations for Apify
More sources for CSV Files
Early access
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