DatriseAI-first ETL

Google Cloud SQL DuckDB

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

How Datrise loads Google Cloud SQL into DuckDB

Datrise syncs Google Cloud SQL's records, events, and configuration objects into DuckDB as a typed table per source entity in a DuckDB file. Flexible or custom fields land in JSON or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP WITH TIME ZONE.

Sync is incremental: Datrise uses rewrites changed entities into the local database (or Parquet) on each run, so re-runs update only what changed. Hive-partitioned Parquet by load date when exporting. DuckDB is single-writer and embedded, so Datrise produces a consistent file snapshot rather than concurrent streaming writes.

Ideal for local and notebook analytics without standing up a server.

Endpoints

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

DuckDB: In-process analytics database for fast local OLAP.

How Google Cloud SQL entities map to DuckDB

Google Cloud SQL entityDuckDB objectNotes
recordsgoogle_cloud_sql_recordsid PK · custom fields → JSON or STRUCT columns
eventsgoogle_cloud_sql_eventsTIMESTAMP WITH TIME ZONE 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 DuckDB?

Flexible values are stored as JSON or STRUCT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native DuckDB types.

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

It runs incrementally — Datrise uses rewrites changed entities into the local database (or Parquet) on each run.

Related pipelines

Early access

Connect Google Cloud SQL to DuckDB the easy way

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