Google Cloud SQL → Snowflake

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

How Datrise loads Google Cloud SQL into Snowflake

Datrise syncs Google Cloud SQL's records, events, and configuration objects into Snowflake as a typed table per source entity. Flexible or custom fields land in VARIANT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP_TZ.

Sync is incremental: Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size, so re-runs update only what changed. Automatic micro-partitioning, with optional clustering keys on high-cardinality ids. Snowflake upper-cases unquoted identifiers, so Datrise standardizes on lower-case quoted names to keep column references stable.

Ideal for central analytics warehouses feeding BI and AI workloads.

Endpoints

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

Snowflake: Cloud data warehouse with separated compute and storage.

How Google Cloud SQL entities map to Snowflake

Google Cloud SQL entity	Snowflake object	Notes
records	`google_cloud_sql_records`	id PK · custom fields → VARIANT columns
events	`google_cloud_sql_events`	TIMESTAMP_TZ events
configuration objects	`google_cloud_sql_configuration_objects`	id PK · linked to google_cloud_sql_records

FAQ

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

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

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

It runs incrementally — Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size.

Related pipelines

Early access

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