π 2025-08-04 β Session: Optimized YouTube Metadata Retrieval and Processing
π 14:30β15:20
π·οΈ Labels: Youtube, API, Python, Automation, Data Processing
π Project: Dev
β 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: