LookML to Mode Integration
AI-first ETL from LookML into Mode. Governed entities, incremental sync, typed landing tables.
How Datrise loads LookML into Mode
Datrise syncs LookML's models, explores, views, dimensions, measures, and joins into Mode as warehouse tables Mode queries with SQL. Flexible or custom fields land in flattened columns for SQL and notebooks, and timestamps such as created, updated, and status changes are typed as temporal columns.
Sync is incremental: Datrise uses incremental refresh of the queried tables, so re-runs update only what changed. Date-partitioned facts for report queries. Mode runs analyst-written SQL, so Datrise lands stable, documented tables that won't break saved reports.
Ideal for SQL-first analysis with Python and R notebooks.
Endpoints
LookML: Semantic model definitions behind Looker content.
Mode: Collaborative analytics workspace for SQL, Python, and shared reports.
How LookML entities map to Mode
| LookML entity | Mode object | Notes |
|---|---|---|
| models | lookml_models | id PK · custom fields → flattened columns for SQL and notebooks |
| 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 Mode?
Flexible values are stored as flattened columns for SQL and notebooks, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Mode types.
How does the LookML to Mode sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the queried tables.
Related pipelines
More destinations for LookML
Early access
Connect LookML to Mode the easy way
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