πŸ“… 2025-08-04 β€” Session: Optimized YouTube Metadata Retrieval and Processing

πŸ•’ 14:30–15:20
🏷️ Labels: Youtube, API, Python, Automation, Data Processing
πŸ“‚ Project: Dev
⭐ Priority: MEDIUM

Session Goal: The session focused on optimizing the retrieval and processing of YouTube video metadata using various tools and methods to enhance efficiency and reduce reliance on traditional API calls.

Key Activities:

  • Explored methods to retrieve video metadata using RSS feeds and the yt-dlp tool, bypassing the Google API client.
  • Utilized yt-dlp for JSON metadata extraction with specific command-line flags for efficient data retrieval.
  • Improved a Python function to fetch the latest YouTube videos using HTTP caching and error handling.
  • Developed a roadmap for web app content orchestration using Next.js and Vercel, focusing on automation and CI/CD.
  • Implemented a backfilling strategy for YouTube videos using yt-dlp and Python.
  • Addressed timezone issues in Python datetime comparisons to ensure accurate data handling.
  • Enhanced YouTube API data retrieval by incorporating additional metadata fields for enriched analytics.
  • Optimized batch processing of YouTube video metadata to minimize API calls and handle pagination.
  • Developed a CSV appending script to handle enriched video records efficiently.

Achievements:

  • Successfully outlined multiple efficient methods for YouTube video metadata retrieval and processing.
  • Established a comprehensive roadmap for web app development and deployment.
  • Enhanced error handling and data processing techniques for robust application performance.

Pending Tasks:

  • Further exploration of Invidious instance’s JSON API for key-free video retrieval.
  • Implementation of the outlined web app roadmap, focusing on content orchestration and deployment.
  • Continuous improvement of batch processing scripts to handle larger datasets effectively.