Sync Google Sheets with Postgres using n8n

Want a reliable, repeatable way to keep a Google Sheet and a Postgres table perfectly synchronized? This guide walks through a ready-to-use n8n workflow that reads Google Sheets, compares the data to a Postgres table, then inserts or updates rows — and notifies your team via Slack. Follow along to automate your Google Sheets to Postgres sync and eliminate manual imports.

Why automate Google Sheets to Postgres sync?

Many teams capture data in Google Sheets because it’s simple and collaborative. But using spreadsheets as the canonical data source introduces risks: inconsistent formatting, missed updates, and duplicated effort when manually importing into a database. Automating the sync delivers clear benefits:

• Reliable, scheduled updates — no manual exports
• Consistent data mapping and validation
• Automatic inserts and updates to avoid duplicates
• Notifications to your team when changes occur

Overview of the n8n workflow

The workflow covered in this post uses n8n nodes to create a robust sync. At a high level the flow:

• Schedule Trigger — run the sync on a recurring schedule
• Retrieve Sheets Data — read rows from Google Sheets
• Select Postgres Rows — retrieve current rows from the Postgres table
• Split Relevant Fields — normalize and isolate the fields you want to compare
• Compare Datasets — detect new rows and changed rows
• Insert Postgres Rows — insert new records
• Update Postgres Rows — update existing records where values changed
• Notify Changes — post a Slack message summarizing the sync

Prerequisites

• An n8n instance (cloud or self-hosted)
• Google account with access to the spreadsheet
• Postgres database credentials and a table to sync to
• Slack webhook or Slack credentials (optional) for notifications

Step-by-step setup

1. Add credentials

In n8n, add the required credentials for Google Sheets and Postgres. For Google Sheets use OAuth or a service account that has read access to the spreadsheet. For Postgres provide host, port, database, user, and password and test the connection.

2. Configure the Schedule Trigger

Use the Schedule Trigger node to define how often you want the sync to run. Common choices:

• Every hour for near real-time sync
• Every night for daily batches

Choose an interval that balances latency and load on your systems.

3. Retrieve Sheets Data

Add a Google Sheets node and set the document ID and sheet (gid). Configure the node to return the range or entire sheet you want to sync. Ensure your header row is consistent and that columns have stable names for mapping — e.g., first_name, last_name, town, age.

4. Select Postgres Rows

Using the Postgres node set to the select operation, fetch the rows from the target table. Pull the columns that you will compare and use as matching keys (for example, first_name and last_name). Returning all rows is typical for a dataset comparison, but consider filtering if your table is very large.

5. Split Relevant Fields

The Split Relevant Fields (Split Out) node lets you normalize and extract only the fields that matter for comparison. This reduces noise and prevents accidental updates from unrelated columns.

6. Compare Datasets

The Compare Datasets node is the heart of the workflow. Configure matching fields (for example, first_name and last_name) so the node can determine whether a Google Sheets row already exists in Postgres. The node outputs three streams:

• In A only — rows only in the Google Sheet (new records to insert)
• In both — rows present in both sources (candidates for update)
• In B only — rows only in Postgres (optional handling if you want to delete or flag)

7. Insert Postgres Rows

Connect the Compare Datasets node’s “In A only” output to a Postgres node with the insert operation. Use auto-mapping or define the column mapping explicitly. Insert only the columns you extracted earlier to keep the operation safe and predictable.

8. Update Postgres Rows

Connect the “In both” output to another Postgres node configured to update rows. Define the matchingColumns (e.g., first_name, last_name) and map the values to the columns you want to update (age, town, etc.). This updates rows where values in Google Sheets differ from Postgres.

9. Notify Changes via Slack

Finally, add a Slack node (or webhook) and post a short summary message like “Rows were inserted or updated by the Google Sheets → Postgres sync workflow.” Optionally, include counts of inserted and updated rows to keep your team informed.

Testing and validation

Before enabling the workflow on a schedule, test manually:

1. Run the Schedule Trigger once to kick off the flow
2. Inspect the output of each node — especially Compare Datasets, Insert, and Update nodes
3. Confirm rows were inserted and updated correctly in Postgres
4. Check Slack notifications (if configured)

Log any unexpected behavior and adjust field mappings or matching keys. If your data contains duplicates or inconsistent names, consider a normalization step (trim, lowercase) before comparison.

Best practices and tips

• Use stable matching keys — avoid using free-text fields when possible; prefer unique IDs if available
• Validate data types — ensure numeric and date fields are converted before writing to Postgres
• Limit the dataset size — if the sheet or table is huge, sync in batches or filter the range
• Implement error handling — catch Postgres errors and notify admins if a sync fails
• Audit trail — add a timestamp or last_synced column in Postgres so you can track when records were updated

Security considerations

Protect credentials and follow the principle of least privilege:

• Create a Postgres user with only the required permissions (SELECT, INSERT, UPDATE)
• Use Google service accounts restricted to the specific spreadsheet
• Store credentials in n8n’s encrypted credential manager (not in plain text nodes)

Troubleshooting

Common issues and fixes:

• Mismatch on field names — ensure headers in Sheets match the column names used in n8n mapping
• Large dataset timeouts — increase node timeouts or break the sync into smaller jobs
• Duplicate rows — add stronger matching logic or a unique ID column

Wrap-up

Automating a Google Sheets to Postgres sync with n8n saves time, reduces errors, and creates a repeatable data pipeline. The template described here is modular — you can add normalization steps, data validation, or advanced notifications. With proper credentials, stable matching keys, and a good testing strategy, this workflow will keep your sheet and database in sync reliably.

Call to action

Ready to set this up? Import the n8n workflow template into your n8n instance, add your Google Sheets and Postgres credentials, and run a manual test. If you want help customizing the workflow for your schema or adding data validation steps, contact us or subscribe to our newsletter for more automation templates and tutorials.

Get started now: import the template, configure credentials, and run the sync. Share your results or questions in the comments below.