LookML to PlanetScale Integration
AI-first ETL from LookML into PlanetScale. Governed entities, incremental sync, typed landing tables.
How Datrise loads LookML into PlanetScale
Datrise syncs LookML's models, explores, views, dimensions, measures, and joins 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
LookML: Semantic model definitions behind Looker content.
PlanetScale: Serverless MySQL platform with safe schema workflows.
How LookML entities map to PlanetScale
| LookML entity | PlanetScale object | Notes |
|---|---|---|
| models | lookml_models | id PK · custom fields → JSON columns |
| explores | lookml_explores | id PK · linked to lookml_models |
| views | lookml_views | id PK · linked to lookml_models |
| dimensions | lookml_dimensions | id PK · linked to lookml_models |
FAQ
How does Datrise handle LookML'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 LookML 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 LookML
More sources for PlanetScale
Early access
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