Looker to PlanetScale Integration
AI-first ETL from Looker into PlanetScale. Governed entities, incremental sync, typed landing tables.
How Datrise loads Looker into PlanetScale
Datrise syncs Looker's looks, dashboards, explores, queries, users, and scheduled plans into PlanetScale as a typed table per source entity. Flexible or custom fields land in JSON columns, and timestamps such as created, updated, and status changes are typed as DATETIME.
Sync is incremental: Datrise uses a watermark on updated-at, applied with INSERT … ON DUPLICATE KEY UPDATE, so re-runs update only what changed. Vitess sharding by tenant or entity key for very large tables. PlanetScale disallows foreign-key constraints by default, so Datrise models relationships by stable id columns rather than enforced FKs.
Ideal for horizontally scalable MySQL apps on Vitess.
Endpoints
Looker: BI platform with a modeled semantic layer.
PlanetScale: Serverless MySQL platform with safe schema workflows.
How Looker entities map to PlanetScale
| Looker entity | PlanetScale object | Notes |
|---|---|---|
| looks | looker_looks | id PK · custom fields → JSON 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 PlanetScale?
Flexible values are stored as JSON columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native PlanetScale types.
How does the Looker to PlanetScale sync stay up to date?
It runs incrementally — Datrise uses a watermark on updated-at, applied with INSERT … ON DUPLICATE KEY UPDATE.
Related pipelines
More destinations for Looker
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