Apify → Snowflake
AI-first ETL from Apify into Snowflake. Governed entities, incremental sync, typed landing tables.
How Datrise loads Apify into Snowflake
Datrise syncs Apify's records, events, and configuration objects into Snowflake as a typed table per source entity. Flexible or custom fields land in VARIANT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP_TZ.
Sync is incremental: Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size, so re-runs update only what changed. Automatic micro-partitioning, with optional clustering keys on high-cardinality ids. Snowflake upper-cases unquoted identifiers, so Datrise standardizes on lower-case quoted names to keep column references stable.
Ideal for central analytics warehouses feeding BI and AI workloads.
Endpoints
Apify: SaaS or API data source for analytics and warehouse sync.
Snowflake: Cloud data warehouse with separated compute and storage.
How Apify entities map to Snowflake
| Apify entity | Snowflake object | Notes |
|---|---|---|
| records | apify_records | id PK · custom fields → VARIANT columns |
| events | apify_events | TIMESTAMP_TZ events |
| configuration objects | apify_configuration_objects | id PK · linked to apify_records |
FAQ
How does Datrise handle Apify's custom fields in Snowflake?
Flexible values are stored as VARIANT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Snowflake types.
How does the Apify to Snowflake sync stay up to date?
It runs incrementally — Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size.
Related pipelines
More destinations for Apify
More sources for Snowflake
Early access
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