DatriseAI-first ETL

Excel File to Looker Integration

AI-first ETL from Excel File into Looker. Governed entities, incremental sync, typed landing tables.

How Datrise loads Excel File into Looker

Datrise syncs Excel File's worksheets, rows, columns, headers, cell ranges, and workbooks into Looker as governed warehouse tables with LookML-ready naming. Flexible or custom fields land in flattened columns (nested fields expanded for modeling), and timestamps such as created, updated, and status changes are typed as date/time dimension columns.

Sync is incremental: Datrise uses incremental refresh of the underlying warehouse tables Looker explores, so re-runs update only what changed. Date-partitioned fact tables for PDT performance. Looker models live in LookML on top of SQL, so Datrise lands clean, stable column names rather than churn that would break your views.

Ideal for governed, version-controlled BI on a warehouse.

Endpoints

Excel File: Spreadsheet workbooks from exports and shared drives.

Looker: Google Cloud BI with LookML semantic models and governed dashboards.

How Excel File entities map to Looker

Excel File entityLooker objectNotes
worksheetsexcel_file_worksheetsid PK · custom fields → flattened columns (nested fields expanded for modeling)
rowsexcel_file_rowsid PK · linked to excel_file_worksheets
columnsexcel_file_columnsid PK · linked to excel_file_worksheets
headersexcel_file_headersid PK · linked to excel_file_worksheets

FAQ

How does Datrise handle Excel File's custom fields in Looker?

Flexible values are stored as flattened columns (nested fields expanded for modeling), so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Looker types.

How does the Excel File to Looker sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the underlying warehouse tables Looker explores.

Related pipelines

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