Synchronize Google Sheets with Postgres Using n8n Automation

Opening Problem Statement

Meet Sarah, a data analyst at a mid-sized retail company. Every week, Sarah manually compares customer data stored in a Google Sheet with the company’s Postgres database to ensure consistency between the two datasets. This repetitive process takes her several precious hours, often leading to accidental errors and outdated records spiraling into business decision delays. The risk of missing critical client updates due to manual syncing causes costly confusion in marketing campaigns and customer outreach.

Sarah’s challenge is specific: how can she reliably, automatically synchronize her Google Sheets customer lists with the Postgres database, keeping both data sources perfectly aligned without lifting a finger?

What This Automation Does

When this n8n workflow runs, it performs a seamless synchronization between Google Sheets and a Postgres database based on scheduled intervals. Here’s what it accomplishes specifically:

• Scheduled Trigger: Automatically triggers every hour to keep data fresh without manual intervention.
• Retrieve Sheets Data: Fetches targeted fields like first name, last name, town, and age from the Google Sheet.
• Select Rows in Postgres: Pulls current records from a specified Postgres table for comparison.
• Compare Datasets: Compares the Google Sheets data to the Postgres records based on first names to determine differences.
• Insert Rows: For new entries found only in Google Sheets, inserts those rows into the Postgres database automatically.
• Update Rows: Updates existing records in Postgres if data differences are detected, maintaining consistency across systems.

The automation could save Sarah up to 5 hours weekly just on manual data reconciliation, drastically reducing errors and expediting reliable customer insights.

Prerequisites ⚙️

• n8n account (self-hosted option available for advanced users)
• Google Sheets account with access to the target spreadsheet📊
• Postgres database access with the correct table and schema configured🔐
• Credentials for Google Sheets and Postgres configured within n8n🔑
• Basic familiarity with n8n’s nodes editor

Step-by-Step Guide

Step 1: Set Up the Schedule Trigger Node

Navigate to your n8n workflow canvas. Click + Add Node → Search for Schedule Trigger. Configure it as follows:

• Interval: Set to every hour (“hours”) to keep your datasets synced hourly.

You should see the trigger ready to run at scheduled intervals initiating the workflow. Common mistake is setting too long an interval which delays syncs.

Step 2: Configure Google Sheets Node to Retrieve Data

Add a Google Sheets node named “Retrieve Sheets Data”.

• Enter your Google Sheets credential.
• Set Document ID to your spreadsheet’s unique ID (example: 1jhUobbdaEuX093J745TsPFMPFbzAIIgx6HnIzdqYqhg).
• Choose the appropriate sheet (e.g., “Sheet1” with gid=0).

After configuring, you should successfully fetch spreadsheet rows. Watch for incorrect document IDs causing errors.

Step 3: Select Rows from Postgres

Add a Postgres node called “Select Rows in Postgres” to pull your existing database records.

• Use your Postgres credentials.
• Set the Schema (usually “public”) and Table (e.g., “testing”).
• Choose Operation: “Select” and enable “Return All” for full data retrieval.

After running, verify it pulls the entire table data. Common slip-ups include wrong schema or missing table permissions.

Step 4: Split Out Relevant Fields

Add the Split Out node named “Split Out Relevant Fields”.

• Set the field to split out as: first_name, last_name, town, age.

This reduces data payload for more efficient comparison. Common mistake: not including all relevant fields causing discrepancies.

Step 5: Compare the Datasets

Add the Compare Datasets node.

• Configure Merge By Fields with first_name in both datasets for comparison.
• Set Resolve to “preferInput1” which prioritizes the Google Sheets data over the Postgres data for conflicts.

You should see two output streams from this node — one for new inserts and another for updates.

Step 6: Insert New Rows into Postgres

Connect the first output of Compare Datasets to a Postgres Insert Rows node called “Insert Rows”.

• Set table and schema same as the “Select Rows” node.
• Enable auto mapping of the fields to columns first_name, last_name, town, age.

This writes new records found only in your Google Sheets into Postgres. Mistake: forgetting to map columns fully.

Step 7: Update Existing Rows in Postgres

Connect the third output of Compare Datasets to a Postgres Update Rows node called “Update Rows”.

• Set the same table and schema.
• Define field mappings explicitly, e.g., age = {{$json.age}}, town = {{$json.town}}, etc.
• Ensure Matching Columns include first_name and last_name so updates affect the correct record.

Check the update runs without error; common mistakes include missing matching columns causing no rows to update.

Customizations ✏️

• Change Sync Interval: In the Schedule Trigger, adjust the interval from hourly to every 30 minutes or daily based on your needs.
• Add More Fields: Modify the Split Out node to include additional fields like “email” or “phone number” and update Postgres mappings accordingly.
• Filter Data: Add a Filter node before Compare Datasets to sync only rows with certain criteria, e.g., town = “New York”.

Troubleshooting 🔧

Problem: “Google Sheets node returns no data”
Cause: Incorrect Document ID or no access permissions.
Solution: Verify your Google Sheets credentials, and double-check the Document ID and Sheet name parameters in the Google Sheets node.

Problem: “Postgres Update Rows does not update records”
Cause: Missing or incorrect matching columns in update node.
Solution: Ensure “Matching Columns” in the Update Rows node include all unique identifiers like “first_name” and “last_name”.

Pre-Production Checklist ✅

• Confirm Google Sheets credentials and access to target spreadsheet.
• Verify Postgres credentials, schema, and table name are correct.
• Test Schedule Trigger firing manually using “Execute Node” feature.
• Run each node individually starting from data retrieval to verify data flows correctly.
• Backup your Postgres database before deploying automated inserts and updates.

Deployment Guide

Once fully configured and tested, activate your workflow in n8n by toggling it to “active”. Since it runs based on a schedule trigger, no manual input is required thereafter.

Monitor logs and execution data in n8n to ensure continuous sync without errors. Adjust schedule settings for optimal performance depending on data volume.

Conclusion

By following this detailed guide, you’ve built a robust n8n automation workflow that synchronizes your Google Sheets data with your Postgres database reliably and automatically. Not only does this save multiple hours each week, but it also reduces data inconsistencies that could impact business processes.

Next steps could include expanding this sync to handle deletions, integrating alerts on sync failures, or syncing other data sources like CRM systems for a comprehensive data pipeline.

With this workflow, you gain peace of mind and more time to focus on insights rather than manual data grunt work.