LookML to PostgreSQL Integration
AI-first ETL from LookML into PostgreSQL. Governed entities, incremental sync, typed landing tables.
How Datrise loads LookML into PostgreSQL
Datrise syncs LookML's models, explores, views, dimensions, measures, and joins into PostgreSQL as a typed table per source entity. Flexible or custom fields land in jsonb columns, and timestamps such as created, updated, and status changes are typed as timestamptz.
Sync is incremental: Datrise uses a watermark on each entity's updated-at, applied with INSERT … ON CONFLICT DO UPDATE, so re-runs update only what changed. Optional declarative range partitioning by load date for high-volume tables. PostgreSQL folds unquoted identifiers to lowercase, so Datrise normalizes mixed-case source fields to snake_case.
Ideal for operational analytics and application backends that need fresh, queryable copies of your data.
Endpoints
LookML: Semantic model definitions behind Looker content.
PostgreSQL: Open-source relational database with strong SQL and extensions.
How LookML entities map to PostgreSQL
| LookML entity | PostgreSQL object | Notes |
|---|---|---|
| models | lookml_models | id PK · custom fields → jsonb 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 PostgreSQL?
Flexible values are stored as jsonb columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native PostgreSQL types.
How does the LookML to PostgreSQL sync stay up to date?
It runs incrementally — Datrise uses a watermark on each entity's updated-at, applied with INSERT … ON CONFLICT DO UPDATE.
Related pipelines
More destinations for LookML
Early access
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