Snowplow to Amazon Redshift Integration
AI-first ETL from Snowplow into Amazon Redshift. Governed entities, incremental sync, typed landing tables.
How Datrise loads Snowplow into Amazon Redshift
Datrise syncs Snowplow's events, users, sessions, contexts, page views, and event schemas into Amazon Redshift as a typed table per source entity. Flexible or custom fields land in SUPER columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMPTZ.
Sync is incremental: Datrise uses COPY from staged files, then a delete-and-insert merge on stable id, so re-runs update only what changed. A DISTKEY on the join id and a SORTKEY on the load timestamp. Redshift performance hinges on dist/sort keys, so Datrise picks them from your entity ids and sync timestamps rather than defaulting to EVEN distribution.
Ideal for AWS-native warehouses already using the Redshift ecosystem.
Endpoints
Snowplow: Behavioral event pipeline with a strict schema.
Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.
How Snowplow entities map to Amazon Redshift
| Snowplow entity | Amazon Redshift object | Notes |
|---|---|---|
| events | snowplow_events | TIMESTAMPTZ events |
| users | snowplow_users | id PK · linked to snowplow_events |
| sessions | snowplow_sessions | id PK · linked to snowplow_events |
| contexts | snowplow_contexts | id PK · linked to snowplow_events |
FAQ
How does Datrise handle Snowplow's custom fields in Amazon Redshift?
Flexible values are stored as SUPER columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon Redshift types.
How does the Snowplow to Amazon Redshift sync stay up to date?
It runs incrementally — Datrise uses COPY from staged files, then a delete-and-insert merge on stable id.
Related pipelines
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Early access
Connect Snowplow to Amazon Redshift the easy way
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