DatriseAI-first ETL

Google Cloud SQL Birst

AI-first ETL from Google Cloud SQL into Birst. Governed entities, incremental sync, typed landing tables.

How Datrise loads Google Cloud SQL into Birst

Datrise syncs Google Cloud SQL's records, events, and configuration objects into Birst as warehouse tables for Birst's automated star schema. 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 source tables Birst ingests, so re-runs update only what changed. Date-partitioned facts. Birst builds its own semantic layer, so Datrise lands conformed, well-keyed tables it can automate against.

Ideal for networked, governed enterprise BI.

Endpoints

Google Cloud SQL: SaaS or API data source for analytics and warehouse sync.

Birst: Cloud BI with networked analytics and enterprise semantic layers.

How Google Cloud SQL entities map to Birst

Google Cloud SQL entityBirst objectNotes
recordsgoogle_cloud_sql_recordsid PK · custom fields → flattened columns
eventsgoogle_cloud_sql_eventsdate/time dimensions events
configuration objectsgoogle_cloud_sql_configuration_objectsid PK · linked to google_cloud_sql_records

FAQ

How does Datrise handle Google Cloud SQL's custom fields in Birst?

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 Birst types.

How does the Google Cloud SQL to Birst sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the source tables Birst ingests.

Related pipelines

Early access

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