Google BigQuery to ThoughtSpot Integration

How Datrise loads Google BigQuery into ThoughtSpot

Datrise syncs Google BigQuery's datasets, tables, views, query jobs, table schemas, and partitions 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

Google BigQuery: Serverless analytics warehouse on Google Cloud.

ThoughtSpot: Search-driven analytics with AI-assisted insights on warehouse data.

How Google BigQuery entities map to ThoughtSpot

Google BigQuery entity	ThoughtSpot object	Notes
datasets	`bigquery_datasets`	id PK · custom fields → flattened columns for searchable fields
tables	`bigquery_tables`	id PK · linked to bigquery_datasets
views	`bigquery_views`	id PK · linked to bigquery_datasets
query jobs	`bigquery_query_jobs`	id PK · linked to bigquery_datasets

FAQ

How does Datrise handle Google BigQuery'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 Google BigQuery to ThoughtSpot sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the indexed tables.

Related pipelines

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