Looker → Yellowfin
AI-first ETL from Looker into Yellowfin. Governed entities, incremental sync, typed landing tables.
How Datrise loads Looker into Yellowfin
Datrise syncs Looker's records, events, and configuration objects 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
Looker: SaaS or API data source for analytics and warehouse sync.
Yellowfin: BI suite with dashboards, automated insights, and data storytelling.
How Looker entities map to Yellowfin
| Looker entity | Yellowfin object | Notes |
|---|---|---|
| records | looker_records | id PK · custom fields → flattened columns |
| events | looker_events | date/time dimensions events |
| configuration objects | looker_configuration_objects | id PK · linked to looker_records |
FAQ
How does Datrise handle Looker'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 Looker to Yellowfin sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
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