Apify to DuckDB Integration
AI-first ETL from Apify into DuckDB. Governed entities, incremental sync, typed landing tables.
How Datrise loads Apify into DuckDB
Datrise syncs Apify's actors, runs, datasets, key-value stores, request queues, and run logs into DuckDB as a typed table per source entity in a DuckDB file. Flexible or custom fields land in JSON or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP WITH TIME ZONE.
Sync is incremental: Datrise uses rewrites changed entities into the local database (or Parquet) on each run, so re-runs update only what changed. Hive-partitioned Parquet by load date when exporting. DuckDB is single-writer and embedded, so Datrise produces a consistent file snapshot rather than concurrent streaming writes.
Ideal for local and notebook analytics without standing up a server.
Endpoints
Apify: Web scraping and browser automation platform.
DuckDB: In-process analytics database for fast local OLAP.
How Apify entities map to DuckDB
| Apify entity | DuckDB object | Notes |
|---|---|---|
| actors | apify_actors | id PK · custom fields → JSON or STRUCT 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 DuckDB?
Flexible values are stored as JSON or STRUCT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native DuckDB types.
How does the Apify to DuckDB sync stay up to date?
It runs incrementally — Datrise uses rewrites changed entities into the local database (or Parquet) on each run.
Related pipelines
More destinations for Apify
Early access
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