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The Google Sheets source syncs spreadsheet rows directly into a Scout table without rebuilding your data elsewhere. It’s a good fit for structured lists that non-technical teams already maintain in a spreadsheet — CRM exports, content inventories, product catalogs, and similar data. Each row becomes a document, and your column headers map to table fields.

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Before You Start

  • Create a Database and a destination table in Scout. See Creating Databases.
  • Make sure your sheet has a clear header row in row 1 — Scout uses these headers as field names.
  • Add table columns for the fields you want to search or filter on.
  • Choose a stable identifier column (for example ID, Slug, or URL) before your first sync. Scout uses it to match and update existing rows; without one, every sync creates duplicates.

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Connect the Google Sheets Integration

1

Open Integrations

Navigate to Integrations in Scout.
2

Connect Google Sheets

Find Google Sheets in the list and start the connection.
3

Grant access

Sign in with your Google account and grant access to your spreadsheets.
Availability may vary by workspace during rollout.

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How Sync Works

Scout reads the sheet top to bottom, treating each row as a document:
  • New rows become new documents.
  • Updated rows are matched by your stable identifier and overwritten — an upsert. Without an identifier, updates create duplicates instead.
  • Deleted rows aren’t removed automatically. Re-run with full replacement if you need to clear them out.
Decide on a stable identifier column before your first sync. ID, Slug, and URL are common choices — anything that uniquely and consistently identifies a row.

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Create a Google Sheets Source

1

Open your Database table

Navigate to the table you want to populate.
2

Add a Source

Click Sources → Add Source, then select Google Sheets.
3

Select the spreadsheet

Choose the spreadsheet and the specific worksheet or tab to sync.
4

Map columns to fields

Match each sheet column to a column in your table. See Field Mapping below.
5

Set sync frequency

Optionally choose a schedule, or leave the source as manual-only.
6

Create the source

Click Create to save the configuration.

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Field Mapping

Map your sheet headers to table column names. A typical content-inventory mapping looks like this:
Map your main text to the content column. Scout embeds this field for semantic search, so retrieval quality depends on it being mapped correctly.
Start with a small subset of columns and expand once you’ve validated data quality.

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Run and Validate

1

Run the source once

Trigger a manual run from the Sources panel.
2

Verify rows were created

Confirm the expected documents appear in your table.
3

Spot-check values

Check a few rows for correct types and formatting.
4

Query the data

Run a query to confirm retrieval quality. See Querying Data.

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Common Issues

Confirm the header row exists in row 1, recheck your field mapping, and make sure column types match the shape of the data.
Add a stable identifier column before re-syncing. Without one, each sync creates new documents instead of updating existing ones.
Review your sync strategy — full replacement versus incremental — and re-run after any major schema changes.
Reconnect with the correct Google account and verify you have at least view access to the spreadsheet.

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Best Practices

  • Keep header names consistent. Renaming a header breaks the mapping until you update the source config.
  • Use an explicit table schema rather than syncing every column in the sheet.
  • Schedule syncs only for sheets that change often. Run manually for one-time imports.
  • Add metadata columns like team, status, or region to support downstream filtering.
Renaming a mapped sheet header or table column breaks the field mapping for that field. The column stays empty on subsequent syncs until you update the mapping.

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Next Steps

Sources

Compare all source types and their sync options.

Querying Data

Search synced data with semantic and hybrid search.

Creating Databases

Design schemas for reliable ingestion.