LookML to Yellowfin Integration
AI-first ETL from LookML into Yellowfin. Governed entities, incremental sync, typed landing tables.
How Datrise loads LookML into Yellowfin
Datrise syncs LookML's models, explores, views, dimensions, measures, and joins into Yellowfin as warehouse tables Yellowfin builds views on. Flexible or custom fields land in flattened columns, and timestamps such as created, updated, and status changes are typed as date/time dimensions.
Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts. Yellowfin views reference columns by name, so Datrise lands stable, well-typed columns to keep reports valid.
Ideal for dashboards with automated data storytelling.
Endpoints
LookML: Semantic model definitions behind Looker content.
Yellowfin: BI suite with dashboards, automated insights, and data storytelling.
How LookML entities map to Yellowfin
| LookML entity | Yellowfin object | Notes |
|---|---|---|
| models | lookml_models | id PK · custom fields → flattened 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 Yellowfin?
Flexible values are stored as flattened columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Yellowfin types.
How does the LookML to Yellowfin sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
Related pipelines
More destinations for LookML
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