YouTube Auto Uploader - Complete Automated System

YouTube Auto Uploader - Complete Automated System

Fully automated video upload system from AWS S3 to YouTube

View Full Project on GitHub

📖 Project Introduction

YouTube Auto Uploader is a fully automated system designed to upload videos from your AWS S3 bucket directly to your YouTube channel without any manual effort.

The system automatically uploads 2 videos per day — one at 9:00 AM and another at 8:00 PM, while also sending email notifications for successful uploads.

🚀 How the Project Works

Complete System Architecture

AWS Serverless YouTube Upload Automation

Complete Workflow

Step Action Description
🕒 1 Scheduled Trigger AWS EventBridge triggers Lambda function daily at 9 AM and 8 PM
📹 2 Video Selection Lambda selects a random video from the S3 bucket
⬇️ 3 Download Video is downloaded from S3 to Lambda's temporary storage
📤 4 YouTube Upload Video is uploaded via the YouTube Data API
📧 5 Notification SNS notification email is sent for success or failure
🧹 6 Cleanup Temporary files are automatically deleted

🌟 Real-Life Automation Benefits

✅ No Manual Work

Fully automated system

✅ Time Saving

Automatically uploads 2 videos daily

✅ Reliable

High reliability using AWS services

✅ Scalable

Can handle 1000+ videos easily

✅ Monitoring

Real-time status via email notifications

🛠️ Technical Stack & AWS Services

1. AWS Lambda

Purpose: Main processing engine

Role: Download videos from S3, upload to YouTube, send notifications

Configuration:

  • Runtime: Python 3.9
  • Memory: 1024 MB
  • Timeout: 15 minutes
  • Handler: lambda_function.lambda_handler

2. Amazon S3

Purpose: Video storage

Role: Central repository for videos

Configuration:

  • Bucket: cartoon-shorts-videos
  • Folder: meo

3. Amazon EventBridge

Purpose: Scheduling service

Role: Automatically triggers Lambda at 9 AM and 8 PM daily

Configuration:

  • Cron Expression: cron(0 9,20 * * ? *)
  • Time Zone: Asia/Kolkata (IST)
  • Rule Name: youtube-daily-uploads

4. Amazon SNS

Purpose: Email notifications

Role: Sends notifications on video upload success/failure

Configuration:

  • Topic Name: youtube-upload-notifications
  • Protocol: Email

5. IAM (Identity and Access Management)

Purpose: Security and permissions

Role: Provides Lambda access to S3 and SNS

Features:

  • Manages secure credentials for all services
  • Least privilege access control
  • Secure service interactions

🌐 External APIs & Libraries

1. YouTube Data API v3

Purpose: Integration with YouTube for uploading videos

Libraries Used:

  • google-api-python-client
  • google-auth-oauthlib
  • google-auth-httplib2

2. Python Libraries

  • boto3 – AWS services interaction
  • google-api-python-client – YouTube API integration
  • google-auth-oauthlib – OAuth 2.0 authentication

🔐 Authentication Setup - Token & Client ID

How YouTube API Credentials Were Obtained

Step 1: Google Cloud Console Setup

  • Create a new project in Google Cloud Console
  • Enable YouTube Data API v3
  • Create OAuth 2.0 Credentials

Step 2: OAuth Consent Screen

  • Select External user type
  • Add Scope: https://www.googleapis.com/auth/youtube.upload
  • Add test users

Step 3: Client Credentials

  • Create OAuth 2.0 Client ID
  • Application Type: Desktop App
  • Download credentials in client_secret.json format

Step 4: Token Generation

  • Run authentication flow locally
  • Log in via Google in browser
  • Approve permissions
  • Token generated and saved in token.json

Credentials Files

  • client_secret.json – Google OAuth credentials
  • token.json – User authentication token

📊 Output & Results

✅ Success Metrics

1. Video Storage

  • 232 videos available in the S3 bucket
  • Organized in meo folder structure
  • Automatic video selection for upload

2. YouTube Uploads

  • Videos successfully uploaded to YouTube
  • Automatic publishing at scheduled times
  • Proper metadata and descriptions included

3. Email Notifications

  • Email notifications working correctly
  • Real-time alerts for upload success/failure
  • Detailed status information included

4. Scheduled Automation

  • Scheduled automation running as expected
  • Reliable execution at 9 AM and 8 PM daily
  • Error handling implemented

🖥️ Sample Output

{
  "status": "PUBLIC_UPLOAD_SUCCESS",
  "message": "Video successfully uploaded to YouTube!",
  "youtube_video_id": "ROKG9xZkJHM",
  "youtube_url": "https://youtu.be/ROKG9xZkJHM",
  "s3_video": "meo/3625273268770745059_68782260754.mp4",
  "timestamp": "2025-01-26 09:00:00 IST"
}

🔧 Maintenance & Monitoring

Regular Checks

  • S3 Bucket Storage – Ensure videos are available
  • YouTube API Quota – Monitor daily limits
  • Lambda Logs – Check for errors in CloudWatch
  • SNS Notifications – Verify email delivery

Troubleshooting

  • Authentication Issues – Refresh token if required
  • YouTube Quota – Wait if daily limit exceeded
  • S3 Permissions – Ensure Lambda has proper access
  • Network Issues – Handle upload failures

Scaling

  • More Videos – Upload additional videos to S3
  • Multiple Channels – Use different credentials for multiple channels
  • Different Schedule – Modify EventBridge rules for scheduling

💡 Future Enhancements

Possible Improvements:

  • Support for multiple YouTube channels
  • Video analytics tracking
  • Automatic thumbnail generation
  • Smart video selection (based on performance metrics)
  • Dashboard for monitoring uploads and statistics

🎯 Conclusion

This project provides complete end-to-end automation for content creators. The system works reliably and eliminates 100% manual effort. Due to AWS serverless architecture, it's cost effective and can automatically scale.

Key Achievements:

  • ✅ Fully Automated - Zero manual intervention
  • ✅ Cost Effective - Pay-per-use pricing
  • ✅ Reliable - AWS managed services
  • ✅ Scalable - Handle unlimited videos
  • ✅ Monitorable - Complete visibility through notifications

Built with ❤️ using AWS Serverless Architecture

View Complete Project on GitHub

MAHIBLOG

Posted by MAHIBLOG