Google Sheets → Amazon S3 Data Lake
AI-first ETL from Google Sheets into Amazon S3 Data Lake. Governed entities, incremental sync, typed landing tables.
How Datrise loads Google Sheets into Amazon S3 Data Lake
Datrise syncs Google Sheets's spreadsheet-based CRM rows, updates, and operational workflow tables 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 Sheets: Spreadsheet CRM workflows and lightweight pipelines.
Amazon S3 Data Lake: Object storage landing zone for parquet and snapshots.
How Google Sheets entities map to Amazon S3 Data Lake
| Google Sheets entity | Amazon S3 Data Lake object | Notes |
|---|---|---|
| spreadsheet-based CRM rows | google_sheets_spreadsheet_based_crm_rows | id PK · custom fields → nested struct/map fields in Parquet |
| updates | google_sheets_updates | id PK · linked to google_sheets_spreadsheet_based_crm_rows |
| operational workflow tables | google_sheets_operational_workflow_tables | id PK · linked to google_sheets_spreadsheet_based_crm_rows |
FAQ
How does Datrise handle Google Sheets'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 Sheets 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 Sheets
- Google Sheets → Azure Data Lake Storage
- Google Sheets → Azure Synapse
- Google Sheets → Spreadsheets
- Google Sheets → Airtable
- Google Sheets → CSV Files
- Google Sheets → MongoDB
- Google Sheets → Supabase
- Google Sheets → Neon
- Google Sheets → PlanetScale
- Google Sheets → Amazon DynamoDB
- Google Sheets → Looker
- Google Sheets → Looker Studio
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Early access
Connect Google Sheets 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.