Google Cloud SQL to ThoughtSpot Integration

How Datrise loads Google Cloud SQL into ThoughtSpot

Datrise syncs Google Cloud SQL's tables, rows, schemas, indexes, read replicas, and binlog changes 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 Cloud SQL: Managed MySQL, PostgreSQL, and SQL Server instances on GCP.

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

How Google Cloud SQL entities map to ThoughtSpot

Google Cloud SQL entity	ThoughtSpot object	Notes
tables	`google_cloud_sql_tables`	id PK · custom fields → flattened columns for searchable fields
rows	`google_cloud_sql_rows`	id PK · linked to google_cloud_sql_tables
schemas	`google_cloud_sql_schemas`	id PK · linked to google_cloud_sql_tables
indexes	`google_cloud_sql_indexes`	id PK · linked to google_cloud_sql_tables

FAQ

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

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

Related pipelines

Early access

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