Google Cloud SQL Postgresql → Amazon S3 Data Lake
AI-first ETL from Google Cloud SQL Postgresql into Amazon S3 Data Lake. Governed entities, incremental sync, typed landing tables.
How Datrise loads Google Cloud SQL Postgresql into Amazon S3 Data Lake
Datrise syncs Google Cloud SQL Postgresql's records, events, and configuration objects 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 Postgresql: SaaS or API data source for analytics and warehouse sync.
Amazon S3 Data Lake: Object storage landing zone for parquet and snapshots.
How Google Cloud SQL Postgresql entities map to Amazon S3 Data Lake
| Google Cloud SQL Postgresql entity | Amazon S3 Data Lake object | Notes |
|---|---|---|
| records | google_cloud_sql_postgresql_records | id PK · custom fields → nested struct/map fields in Parquet |
| events | google_cloud_sql_postgresql_events | ISO-8601 timestamp columns events |
| configuration objects | google_cloud_sql_postgresql_configuration_objects | id PK · linked to google_cloud_sql_postgresql_records |
FAQ
How does Datrise handle Google Cloud SQL Postgresql'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 Postgresql 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 Postgresql
- Google Cloud SQL Postgresql → Azure Data Lake Storage
- Google Cloud SQL Postgresql → Azure Synapse
- Google Cloud SQL Postgresql → Spreadsheets
- Google Cloud SQL Postgresql → Airtable
- Google Cloud SQL Postgresql → CSV Files
- Google Cloud SQL Postgresql → MongoDB
- Google Cloud SQL Postgresql → Supabase
- Google Cloud SQL Postgresql → Neon
- Google Cloud SQL Postgresql → PlanetScale
- Google Cloud SQL Postgresql → Amazon DynamoDB
- Google Cloud SQL Postgresql → Looker
- Google Cloud SQL Postgresql → Looker Studio
More sources for Amazon S3 Data Lake
- Google Cloud Storage F → Amazon S3 Data Lake
- Google Directory → Amazon S3 Data Lake
- Google Ecommerce → Amazon S3 Data Lake
- Google Pagespeed Insights → Amazon S3 Data Lake
- Google Webfonts → Amazon S3 Data Lake
- Greenhouse → Amazon S3 Data Lake
- Gridly → Amazon S3 Data Lake
- Gutendex → 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
Early access
Connect Google Cloud SQL Postgresql 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.