DatriseAI-first ETL

Apify to PostgreSQL Integration

AI-first ETL from Apify into PostgreSQL. Governed entities, incremental sync, typed landing tables.

How Datrise loads Apify into PostgreSQL

Datrise syncs Apify's actors, runs, datasets, key-value stores, request queues, and run logs into PostgreSQL as a typed table per source entity. Flexible or custom fields land in jsonb columns, and timestamps such as created, updated, and status changes are typed as timestamptz.

Sync is incremental: Datrise uses a watermark on each entity's updated-at, applied with INSERT … ON CONFLICT DO UPDATE, so re-runs update only what changed. Optional declarative range partitioning by load date for high-volume tables. PostgreSQL folds unquoted identifiers to lowercase, so Datrise normalizes mixed-case source fields to snake_case.

Ideal for operational analytics and application backends that need fresh, queryable copies of your data.

Endpoints

Apify: Web scraping and browser automation platform.

PostgreSQL: Open-source relational database with strong SQL and extensions.

How Apify entities map to PostgreSQL

Apify entityPostgreSQL objectNotes
actorsapify_actorsid PK · custom fields → jsonb columns
runsapify_runsid PK · linked to apify_actors
datasetsapify_datasetsid PK · linked to apify_actors
key-value storesapify_key_value_storesid PK · linked to apify_actors

FAQ

How does Datrise handle Apify's custom fields in PostgreSQL?

Flexible values are stored as jsonb columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native PostgreSQL types.

How does the Apify to PostgreSQL sync stay up to date?

It runs incrementally — Datrise uses a watermark on each entity's updated-at, applied with INSERT … ON CONFLICT DO UPDATE.

Related pipelines

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