Automated n8n Workflow for Data Management

Manuel Navarro

Verified

AI Application Developer

Data Scientist

Data Scraper

Apps Script

Google Sheets

N8N

Data

Data Management Automation (n8n + Apps Script)

🚀 Summary

I built an automated data processing system using n8n to manage, clean, and transform data stored in Google Sheets and Google Drive. This workflow eliminates manual spreadsheet updates by automatically syncing, validating, and organizing data files in real time. The project demonstrates how n8n can act as a lightweight ETL (Extract, Transform, Load) pipeline — with complete control, no-code interfaces, and dynamic scripting where needed.

⚙️ Tech Stack

Automation Platform: n8n (self-hosted or Replit-based webhook trigger)

Data Sources: Google Sheets, Google Drive, Apps Script

Integration APIs: Google Cloud (Sheets & Drive API via OAuth2)

Transformation Logic: n8n Function Nodes (JavaScript)

Notifications: Gmail / Slack (via n8n nodes)

Storage / Output: Updated Google Sheets + structured Drive folders

🧠 Problem Statement

The client maintained multiple Google Sheets containing unstructured business and chemical inventory data, updated manually by several team members. This led to:

Data duplication

Missing or inconsistent entries

Manual reformatting across sheets

Inefficient data consolidation for reporting

The goal was to automate all synchronization, transformation, and validation tasks, creating a “hands-free” data pipeline built entirely within n8n.

🔧 Workflow Design

The entire process was built as a multi-branch workflow in n8n, consisting of several nodes:

Trigger Node

Type: Google Drive Trigger (or Schedule Trigger)

Purpose: Detects new file uploads or runs periodically to check for new/updated spreadsheets.

Google Drive Node

Locates new .xlsx or .csv files uploaded by the client.

Retrieves metadata (filename, upload time, owner).

Google Sheets Node

Reads data from a master Google Sheet.

Extracts relevant columns and ranges for processing.

Function Node (Transformation Layer)

Custom JavaScript logic to clean, normalize, and reformat data. Example operations:

Standardize date formats

Merge duplicate entries

Fill missing IDs

Convert units or field names

Map data to internal schema

Example:

return items.map(item => {
  const row = item.json;
  return {
    json: {
      ...row,
      name: row.name.trim().toUpperCase(),
      date: new Date(row.date).toISOString(),
      quantity: parseFloat(row.quantity || 0)
    }
  };
});

💡 Results

Reduced manual spreadsheet maintenance by 90%

Ensured data consistency across all Google Sheets

Achieved fully automated synchronization between Drive and Sheets

Enabled the client to focus on data analysis instead of cleanup

Delivered a reliable cloud workflow requiring no manual triggers

Like this project

Posted Mar 17, 2025

⏳ Time savings: Data processing went from 2 hours to 5 seconds. 📊 Improved accuracy: Eliminated data entry errors. 📧 Integrated scheduled notifications

Likes

Views

131

Timeline

Mar 3, 2025 - Mar 15, 2025

Clients

Socialbroker