This article provides a complete, practical walkthrough of the Splitout Postgres Sync Scheduled n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:
   How to Automatically Sync Google Sheets Data with PostgreSQL Using n8n
   
   Meta Description:
   Learn how to set up a powerful no-code automation using n8n to synchronize data between Google Sheets and a PostgreSQL database. Keep your records up-to-date with minimal manual intervention.
   
   Keywords:
   n8n automation, Google Sheets sync, PostgreSQL integration, no-code workflow, data synchronization, Google Sheets to Postgres, n8n tutorial, compare datasets, automated data pipeline
   
   Third-party APIs Used:
   
   - Google Sheets API
   - PostgreSQL (via n8n’s Postgres node)
   
   Article:
   
   Automatically Synchronize Google Sheets with PostgreSQL Using n8n
   
   Data fragmentation is a common hurdle facing data-driven teams. It often begins with spreadsheets used for tracking, planning, and collaboration—and ends with more scalable systems like relational databases for production use. Yet, keeping both ecosystems synchronized remains a time-consuming task for many. Thankfully, automation tools like n8n provide a simple, scalable way to bridge this gap.
   
   In this article, you'll learn how to create a no-code automation using n8n that automatically updates your PostgreSQL database with new or modified rows from a Google Sheet. If you're managing dynamic data like contacts or leads, this tutorial will help you streamline your pipeline and improve data integrity—without needing to write custom code.
   
   Overview of the Workflow
   
   This n8n workflow checks for updates in a Google Sheet at regular intervals, compares the Sheet's contents to what's already in a PostgreSQL database, and updates or inserts new records accordingly.
   
   Let’s look at how it all works, step-by-step.
   
   🕒 1. Schedule Trigger
   
   The automation begins with a Schedule Trigger node. This node controls when the sync is executed. In this example, the workflow is configured to run every few hours. You can customize this to daily or even every few minutes depending on your needs.
   
   📄 2. Retrieve Data from Google Sheets
   
   The Retrieve Sheets Data node connects to a specified Google Sheet. To make this work, you'll need to provide your Google Sheets API credentials and select the document and sheet ID. In this case, the sheet is named “Sheet1” inside a document called “Testing_Sheet.”
   
   🧩 3. Split Out Relevant Fields
   
   Once the Google Sheets data is retrieved, the next node—Split Out Relevant Fields—extracts the columns we care about: first_name, last_name, town, and age.
   
   💾 4. Retrieve Data from Postgres
   
   At the same time, the workflow also queries the PostgreSQL database using the Select Rows node. This selects all rows from the “testing” table in the “public” schema.
   
   ⚖️ 5. Compare the Datasets
   
   This is the heart of the automation. The Compare Datasets node receives input from the Split Out Relevant Fields node (Google Sheets) as well as the Select Rows node (PostgreSQL). The two datasets are compared using the first_name field as a unique identifier (though you can customize this). Any discrepancies—like newly added rows or modified entries—are passed on for further processing.
   
   ↪️ 6. Insert New Rows into PostgreSQL
   
   Detected rows that don't already exist in the database are routed to the Insert Rows node. This node maps incoming data to the PostgreSQL columns and inserts the new records into the correct table.
   
   ♻️ 7. Update Existing Rows in PostgreSQL
   
   Rows that exist but have different data—maybe “age” or “town” changed—are sent to the Update Rows node. It uses the first_name and last_name fields for matching and updates columns like age, town, and last_name with the new values.
   
   📝 Helpful Notes and Configuration
   
   The workflow includes a few sticky notes to assist with customization:
   
   - One note outlines how to set up your credentials for Google Sheets and PostgreSQL, making sure the correct tables and documents are selected.
   - Another encourages users to define proper update logic by ensuring the Update and Insert nodes are pointing to the same database table.
   - A third explains how to cleanly split and prepare the necessary fields before comparison.
   
   Why Use This Workflow?
   
   - No-Code Simplicity: Complex logic without writing a single line of code.
   - Scalable & Modular: Add new fields, filters, or logic as needed.
   - Real-Time Data Syncing: Run this hourly or daily to always keep your database in sync.
   - Error Reduction: Say goodbye to manual copy-paste errors.
   
   Use Cases
   
   - Sales teams using Google Sheets to track leads that need to be loaded into a CRM backed by Postgres.
   - HR departments syncing applicant or employee records for review.
   - Schools maintaining data in Google Sheets that must reflect in internal systems.
   
   Final Thoughts
   
   With n8n and a few configuration steps, you can automate the tedious and error-prone process of keeping your spreadsheets and databases synchronized. Whether you're splicing together team spreadsheets with back-end databases or building a lightweight ETL pipeline, this workflow is a valuable blueprint for low-code data integration.
   
   By leveraging the Google Sheets and PostgreSQL nodes inside n8n, and combining them with logical nodes like Compare Datasets and Split Out Relevant Fields, you can gain tight control over your data ecosystem with minimal setup effort.
   
   Ready to build your own data sync? Just plug in your credentials, adjust a few node parameters, and you're off to the races!
   
   👨‍💻 Pro Tip: Make sure your field names align perfectly between your Sheet and your Postgres table to avoid unexpected results. Also, consider adding logging or notifications to track successful runs or errors.
   
   Now, you're just one click away from seamless spreadsheet-to-database automation.

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.