Looker to Redash Integration
AI-first ETL from Looker into Redash. Governed entities, incremental sync, typed landing tables.
How Datrise loads Looker into Redash
Datrise syncs Looker's looks, dashboards, explores, queries, users, and scheduled plans into Redash as SQL tables Redash queries and visualizes. Flexible or custom fields land in flattened columns for query results, and timestamps such as created, updated, and status changes are typed as temporal columns.
Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts for scheduled queries. Redash caches query results on a schedule, so Datrise keeps tables incrementally fresh so cached dashboards reflect reality.
Ideal for lightweight, query-driven dashboards.
Endpoints
Looker: BI platform with a modeled semantic layer.
Redash: Open-source SQL client for queries, visualizations, and dashboards.
How Looker entities map to Redash
| Looker entity | Redash object | Notes |
|---|---|---|
| looks | looker_looks | id PK · custom fields → flattened columns for query results |
| 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 Redash?
Flexible values are stored as flattened columns for query results, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Redash types.
How does the Looker to Redash sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
Related pipelines
More destinations for Looker
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