Google Cloud SQL to Amazon S3 Data Lake Integration
AI-first ETL from Google Cloud SQL into Amazon S3 Data Lake. Governed entities, incremental sync, typed landing tables.
How Datrise loads Google Cloud SQL into Amazon S3 Data Lake
Datrise syncs Google Cloud SQL's tables, rows, schemas, indexes, read replicas, and binlog changes into Amazon S3 Data Lake as columnar Parquet objects partitioned per source entity. Flexible or custom fields land in nested struct/map fields in Parquet, and timestamps such as created, updated, and status changes are typed as ISO-8601 timestamp columns.
Sync is incremental: Datrise uses writes new date partitions and compacts small files on a schedule, so re-runs update only what changed. Hive-style path partitioning (entity/date) for engine-agnostic reads. A lake has no schema enforcement, so Datrise writes a schema manifest alongside the data to keep downstream engines consistent.
Ideal for an open, engine-neutral storage layer for Spark, Athena, Trino, or DuckDB.
Endpoints
Google Cloud SQL: Managed MySQL, PostgreSQL, and SQL Server instances on GCP.
Amazon S3 Data Lake: Object storage landing zone for parquet and snapshots.
How Google Cloud SQL entities map to Amazon S3 Data Lake
| Google Cloud SQL entity | Amazon S3 Data Lake object | Notes |
|---|---|---|
| tables | google_cloud_sql_tables | id PK · custom fields → nested struct/map fields in Parquet |
| 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 Amazon S3 Data Lake?
Flexible values are stored as nested struct/map fields in Parquet, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon S3 Data Lake types.
How does the Google Cloud SQL to Amazon S3 Data Lake sync stay up to date?
It runs incrementally — Datrise uses writes new date partitions and compacts small files on a schedule.
Related pipelines
More destinations for Google Cloud SQL
- 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
- Google Cloud SQL → Supabase
- Google Cloud SQL → Neon
- Google Cloud SQL → PlanetScale
- Google Cloud SQL → Amazon DynamoDB
- Google Cloud SQL → Looker
- Google Cloud SQL → Looker Studio
More sources for Amazon S3 Data Lake
- Google Cloud Storage → Amazon S3 Data Lake
- Google Directory → Amazon S3 Data Lake
- Google Ecommerce → Amazon S3 Data Lake
- Google PageSpeed Insights → Amazon S3 Data Lake
- Greenhouse → Amazon S3 Data Lake
- Gridly → Amazon S3 Data Lake
- Harness → Amazon S3 Data Lake
- Harvest Forecast → Amazon S3 Data Lake
- Heap → Amazon S3 Data Lake
- Hellobaton → Amazon S3 Data Lake
- Heroku → Amazon S3 Data Lake
- PostgreSQL → Amazon S3 Data Lake
Early access
Connect Google Cloud SQL to Amazon S3 Data Lake 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.