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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:
Sheet headerTable columnNotes
TitletitleUsed in search results
Page URLurlLinks back to the source
SummarycontentMain text for retrieval
Last Updatedupdated_atHelps with freshness ranking
Owner TeamteamUseful for filtering
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.