Google Cloud SQL to Google BigQuery Integration
AI-first ETL from Google Cloud SQL into Google BigQuery. Governed entities, incremental sync, typed landing tables.
How Datrise loads Google Cloud SQL into Google BigQuery
Datrise syncs Google Cloud SQL's tables, rows, schemas, indexes, read replicas, and binlog changes into Google BigQuery as a partitioned table per source entity. Flexible or custom fields land in JSON or nested/repeated (STRUCT) columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.
Sync is incremental: Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target, so re-runs update only what changed. Partition by ingestion or event date and cluster by entity id to keep scanned bytes low. BigQuery bills by bytes scanned, so Datrise partitions and clusters every table to keep query costs predictable.
Ideal for Google-stack analytics and ML on serverless infrastructure.
Endpoints
Google Cloud SQL: Managed MySQL, PostgreSQL, and SQL Server instances on GCP.
Google BigQuery: Serverless analytics warehouse on GCP.
How Google Cloud SQL entities map to Google BigQuery
| Google Cloud SQL entity | Google BigQuery object | Notes |
|---|---|---|
| tables | google_cloud_sql_tables | id PK · custom fields → JSON or nested/repeated (STRUCT) columns |
| 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 Google BigQuery?
Flexible values are stored as JSON or nested/repeated (STRUCT) columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Google BigQuery types.
How does the Google Cloud SQL to Google BigQuery sync stay up to date?
It runs incrementally — Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target.
Related pipelines
More destinations for Google Cloud SQL
- Google Cloud SQL → Amazon Redshift
- Google Cloud SQL → Databricks SQL Warehouse
- Google Cloud SQL → ClickHouse
- Google Cloud SQL → DuckDB
- Google Cloud SQL → Amazon Athena
- Google Cloud SQL → Amazon S3 Data Lake
- Google Cloud SQL → Azure Data Lake Storage
- Google Cloud SQL → Azure Synapse
- Google Cloud SQL → Spreadsheets
- Google Cloud SQL → Airtable
- Google Cloud SQL → CSV Files
- Google Cloud SQL → MongoDB
More sources for Google BigQuery
- Google Cloud Storage → Google BigQuery
- Google Directory → Google BigQuery
- Google Ecommerce → Google BigQuery
- Google PageSpeed Insights → Google BigQuery
- Greenhouse → Google BigQuery
- Gridly → Google BigQuery
- Harness → Google BigQuery
- Harvest Forecast → Google BigQuery
- Heap → Google BigQuery
- Hellobaton → Google BigQuery
- Heroku → Google BigQuery
- PostgreSQL → Google BigQuery
Early access
Connect Google Cloud SQL to Google BigQuery the easy way
Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.