DatriseAI-first ETL

Excel File to Amazon Redshift Integration

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

How Datrise loads Excel File into Amazon Redshift

Datrise syncs Excel File's worksheets, rows, columns, headers, cell ranges, and workbooks into Amazon Redshift as a typed table per source entity. Flexible or custom fields land in SUPER columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMPTZ.

Sync is incremental: Datrise uses COPY from staged files, then a delete-and-insert merge on stable id, so re-runs update only what changed. A DISTKEY on the join id and a SORTKEY on the load timestamp. Redshift performance hinges on dist/sort keys, so Datrise picks them from your entity ids and sync timestamps rather than defaulting to EVEN distribution.

Ideal for AWS-native warehouses already using the Redshift ecosystem.

Endpoints

Excel File: Spreadsheet workbooks from exports and shared drives.

Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.

How Excel File entities map to Amazon Redshift

Excel File entityAmazon Redshift objectNotes
worksheetsexcel_file_worksheetsid PK · custom fields → SUPER columns
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 Amazon Redshift?

Flexible values are stored as SUPER columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon Redshift types.

How does the Excel File to Amazon Redshift sync stay up to date?

It runs incrementally — Datrise uses COPY from staged files, then a delete-and-insert merge on stable id.

Related pipelines

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