DatriseAI-first ETL

Google Sheets Sisense

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

How Datrise loads Google Sheets into Sisense

Datrise syncs Google Sheets's spreadsheet-based CRM rows, updates, and operational workflow tables into Sisense as modeled tables for a Sisense ElastiCube (or live connection). Flexible or custom fields land in flattened columns for the cube, and timestamps such as created, updated, and status changes are typed as date/time fields.

Sync is incremental: Datrise uses incremental ElastiCube builds on changed rows, so re-runs update only what changed. Date-partitioned facts to speed cube builds. ElastiCube is an in-memory model, so Datrise lands incremental, build-friendly tables rather than forcing full rebuilds.

Ideal for embedded analytics on an in-memory engine.

Endpoints

Google Sheets: Spreadsheet CRM workflows and lightweight pipelines.

Sisense: Analytics platform with elastic data models and embedded analytics.

How Google Sheets entities map to Sisense

Google Sheets entitySisense objectNotes
spreadsheet-based CRM rowsgoogle_sheets_spreadsheet_based_crm_rowsid PK · custom fields → flattened columns for the cube
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 Sisense?

Flexible values are stored as flattened columns for the cube, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Sisense types.

How does the Google Sheets to Sisense sync stay up to date?

It runs incrementally — Datrise uses incremental ElastiCube builds on changed rows.

Related pipelines

Early access

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