DatriseAI-first ETL

Google Sheets Amazon Athena

AI-first ETL from Google Sheets into Amazon Athena. Governed entities, incremental sync, typed landing tables.

How Datrise loads Google Sheets into Amazon Athena

Datrise syncs Google Sheets's spreadsheet-based CRM rows, updates, and operational workflow tables into Amazon Athena as partitioned Parquet in S3 exposed as an Athena table. Flexible or custom fields land in struct/map columns in Parquet, and timestamps such as created, updated, and status changes are typed as timestamp.

Sync is incremental: Datrise uses writes new Parquet partitions and registers them in the Glue Data Catalog, so re-runs update only what changed. Hive-style partitioning by load date so Athena scans only new data. Athena bills per byte scanned and small files hurt, so Datrise compacts to right-sized Parquet rather than many tiny objects.

Ideal for serverless SQL over an S3 lake without a running warehouse.

Endpoints

Google Sheets: Spreadsheet CRM workflows and lightweight pipelines.

Amazon Athena: Serverless SQL over S3 data lake tables.

How Google Sheets entities map to Amazon Athena

Google Sheets entityAmazon Athena objectNotes
spreadsheet-based CRM rowsgoogle_sheets_spreadsheet_based_crm_rowsid PK · custom fields → struct/map columns in Parquet
updatesgoogle_sheets_updatesid PK · linked to google_sheets_spreadsheet_based_crm_rows
operational workflow tablesgoogle_sheets_operational_workflow_tablesid PK · linked to google_sheets_spreadsheet_based_crm_rows

FAQ

How does Datrise handle Google Sheets's custom fields in Amazon Athena?

Flexible values are stored as struct/map columns in Parquet, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon Athena types.

How does the Google Sheets to Amazon Athena sync stay up to date?

It runs incrementally — Datrise uses writes new Parquet partitions and registers them in the Glue Data Catalog.

Related pipelines

Early access

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