Google Drive → Amazon S3 Data Lake
AI-first ETL from Google Drive into Amazon S3 Data Lake. Governed entities, incremental sync, typed landing tables.
How Datrise loads Google Drive into Amazon S3 Data Lake
Datrise syncs Google Drive's files, folders, permissions, and revision metadata 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 Drive: Cloud file storage often used for operational exports.
Amazon S3 Data Lake: Object storage landing zone for parquet and snapshots.
How Google Drive entities map to Amazon S3 Data Lake
| Google Drive entity | Amazon S3 Data Lake object | Notes |
|---|---|---|
| files | google_drive_files | id PK · custom fields → nested struct/map fields in Parquet |
| folders | google_drive_folders | id PK · linked to google_drive_files |
| permissions | google_drive_permissions | id PK · linked to google_drive_files |
| revision metadata | google_drive_revision_metadata | id PK · linked to google_drive_files |
FAQ
How does Datrise handle Google Drive'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 Drive 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 Drive
- Google Drive → Azure Data Lake Storage
- Google Drive → Azure Synapse
- Google Drive → Spreadsheets
- Google Drive → Airtable
- Google Drive → CSV Files
- Google Drive → MongoDB
- Google Drive → Supabase
- Google Drive → Neon
- Google Drive → PlanetScale
- Google Drive → Amazon DynamoDB
- Google Drive → Looker
- Google Drive → Looker Studio
More sources for Amazon S3 Data Lake
- Google Search Console → Amazon S3 Data Lake
- Harvest → Amazon S3 Data Lake
- Iterable → Amazon S3 Data Lake
- Klaviyo → Amazon S3 Data Lake
- Adobe Commerce (Magento) → Amazon S3 Data Lake
- Mixpanel → Amazon S3 Data Lake
- NetSuite → Amazon S3 Data Lake
- Pardot → Amazon S3 Data Lake
- Pendo → Amazon S3 Data Lake
- QuickBooks → Amazon S3 Data Lake
- Recharge → Amazon S3 Data Lake
- Recurly → Amazon S3 Data Lake
Early access
Connect Google Drive 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.