DatriseAI-first ETL

Snowplow to PostgreSQL Integration

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

How Datrise loads Snowplow into PostgreSQL

Datrise syncs Snowplow's events, users, sessions, contexts, page views, and event schemas 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

Snowplow: Behavioral event pipeline with a strict schema.

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

How Snowplow entities map to PostgreSQL

Snowplow entityPostgreSQL objectNotes
eventssnowplow_eventstimestamptz events
userssnowplow_usersid PK · linked to snowplow_events
sessionssnowplow_sessionsid PK · linked to snowplow_events
contextssnowplow_contextsid PK · linked to snowplow_events

FAQ

How does Datrise handle Snowplow'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 Snowplow 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

Early access

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