DatriseAI-first ETL

Snowplow PostgreSQL

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

How Datrise loads Snowplow into PostgreSQL

Datrise syncs Snowplow's records, events, and configuration objects 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: SaaS or API data source for analytics and warehouse sync.

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

How Snowplow entities map to PostgreSQL

Snowplow entityPostgreSQL objectNotes
recordssnowplow_recordsid PK · custom fields → jsonb columns
eventssnowplow_eventstimestamptz events
configuration objectssnowplow_configuration_objectsid PK · linked to snowplow_records

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

Connect Snowplow to PostgreSQL the easy way

Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.