Apify to Neon Integration
AI-first ETL from Apify into Neon. Governed entities, incremental sync, typed landing tables.
How Datrise loads Apify into Neon
Datrise syncs Apify's actors, runs, datasets, key-value stores, request queues, and run logs into Neon 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 updated-at, applied with INSERT … ON CONFLICT DO UPDATE, so re-runs update only what changed. Optional declarative partitioning by load date. Neon separates compute from storage, so Datrise batches writes to keep autoscaling compute from cold-starting on every small change.
Ideal for serverless Postgres workloads that scale to zero between syncs.
Endpoints
Apify: Web scraping and browser automation platform.
Neon: Serverless Postgres destination with branching and autoscaling.
How Apify entities map to Neon
| Apify entity | Neon object | Notes |
|---|---|---|
| actors | apify_actors | id PK · custom fields → jsonb 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 Neon?
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 Neon types.
How does the Apify to Neon sync stay up to date?
It runs incrementally — Datrise uses a watermark on updated-at, applied with INSERT … ON CONFLICT DO UPDATE.
Related pipelines
More destinations for Apify
Early access
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