Google Cloud Storage to Amazon S3 Data Lake Integration
AI-first ETL from Google Cloud Storage into Amazon S3 Data Lake. Governed entities, incremental sync, typed landing tables.
How Datrise loads Google Cloud Storage into Amazon S3 Data Lake
Datrise syncs Google Cloud Storage's buckets, objects, prefixes, object metadata, generations, and storage classes 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 Storage: Object storage for files and data lake exports on GCP.
Amazon S3 Data Lake: Object storage landing zone for parquet and snapshots.
How Google Cloud Storage entities map to Amazon S3 Data Lake
| Google Cloud Storage entity | Amazon S3 Data Lake object | Notes |
|---|---|---|
| buckets | google_cloud_storage_f_buckets | id PK · custom fields → nested struct/map fields in Parquet |
| objects | google_cloud_storage_f_objects | id PK · linked to google_cloud_storage_f_buckets |
| prefixes | google_cloud_storage_f_prefixes | id PK · linked to google_cloud_storage_f_buckets |
| object metadata | google_cloud_storage_f_object_metadata | id PK · linked to google_cloud_storage_f_buckets |
FAQ
How does Datrise handle Google Cloud Storage'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 Storage 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 Storage
- Google Cloud Storage → Azure Data Lake Storage
- Google Cloud Storage → Azure Synapse
- Google Cloud Storage → Spreadsheets
- Google Cloud Storage → Airtable
- Google Cloud Storage → CSV Files
- Google Cloud Storage → MongoDB
- Google Cloud Storage → Supabase
- Google Cloud Storage → Neon
- Google Cloud Storage → PlanetScale
- Google Cloud Storage → Amazon DynamoDB
- Google Cloud Storage → Looker
- Google Cloud Storage → Looker Studio
More sources for 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
- Hubplanner → Amazon S3 Data Lake
Early access
Connect Google Cloud Storage 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.