Looker to Amazon Redshift Integration
AI-first ETL from Looker into Amazon Redshift. Governed entities, incremental sync, typed landing tables.
How Datrise loads Looker into Amazon Redshift
Datrise syncs Looker's looks, dashboards, explores, queries, users, and scheduled plans 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
Looker: BI platform with a modeled semantic layer.
Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.
How Looker entities map to Amazon Redshift
| Looker entity | Amazon Redshift object | Notes |
|---|---|---|
| looks | looker_looks | id PK · custom fields → SUPER columns |
| dashboards | looker_dashboards | id PK · linked to looker_looks |
| explores | looker_explores | id PK · linked to looker_looks |
| queries | looker_queries | id PK · linked to looker_looks |
FAQ
How does Datrise handle Looker'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 Looker 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.
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Early access
Connect Looker to Amazon Redshift the easy way
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