LookML to Chartio Integration
AI-first ETL from LookML into Chartio. Governed entities, incremental sync, typed landing tables.
How Datrise loads LookML into Chartio
Datrise syncs LookML's models, explores, views, dimensions, measures, and joins into Chartio as SQL tables a visual-SQL explorer connects to. Flexible or custom fields land in flattened columns for visual SQL, 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. Visual-SQL tools build joins from your schema, so Datrise lands clearly related tables with stable id columns.
Ideal for drag-and-drop charting over a database.
Endpoints
LookML: Semantic model definitions behind Looker content.
Chartio: Cloud BI for exploring warehouse data with drag-and-drop charts.
How LookML entities map to Chartio
| LookML entity | Chartio object | Notes |
|---|---|---|
| models | lookml_models | id PK · custom fields → flattened columns for visual SQL |
| 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 Chartio?
Flexible values are stored as flattened columns for visual SQL, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Chartio types.
How does the LookML to Chartio sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
Related pipelines
More destinations for LookML
Early access
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