LookML to ThoughtSpot Integration
AI-first ETL from LookML into ThoughtSpot. Governed entities, incremental sync, typed landing tables.
How Datrise loads LookML into ThoughtSpot
Datrise syncs LookML's models, explores, views, dimensions, measures, and joins into ThoughtSpot as warehouse tables ThoughtSpot indexes for search. Flexible or custom fields land in flattened columns for searchable fields, and timestamps such as created, updated, and status changes are typed as date/time columns.
Sync is incremental: Datrise uses incremental refresh of the indexed tables, so re-runs update only what changed. Date-partitioned facts for live-query performance. ThoughtSpot search relies on clear names and relationships, so Datrise lands well-named, joinable tables.
Ideal for natural-language search analytics over a warehouse.
Endpoints
LookML: Semantic model definitions behind Looker content.
ThoughtSpot: Search-driven analytics with AI-assisted insights on warehouse data.
How LookML entities map to ThoughtSpot
| LookML entity | ThoughtSpot object | Notes |
|---|---|---|
| models | lookml_models | id PK · custom fields → flattened columns for searchable fields |
| 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 ThoughtSpot?
Flexible values are stored as flattened columns for searchable fields, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native ThoughtSpot types.
How does the LookML to ThoughtSpot sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the indexed tables.
Related pipelines
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