Segment → Amazon Redshift
AI-first ETL from Segment into Amazon Redshift. Governed entities, incremental sync, typed landing tables.
How Datrise loads Segment into Amazon Redshift
Datrise syncs Segment's sources, destinations, track events, identify calls, and schema catalog 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
Segment: Customer data platform routing events to warehouses.
Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.
How Segment entities map to Amazon Redshift
| Segment entity | Amazon Redshift object | Notes |
|---|---|---|
| sources | segment_sources | id PK · custom fields → SUPER columns |
| destinations | segment_destinations | id PK · linked to segment_sources |
| track events | segment_track_events | TIMESTAMPTZ events |
| identify calls | segment_identify_calls | id PK · linked to segment_sources |
FAQ
How does Datrise handle Segment'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 Segment 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
More destinations for Segment
More sources for Amazon Redshift
Early access
Connect Segment to Amazon Redshift the easy way
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