Music App - Pagalworld

Project Overview

Developed a music application leveraging self-scraped data and automated extraction of music metadata, including album art. Built with the SERN stack, incorporating Zustand for efficient state management, and optimized for seamless performance across all devices and themes.

Key Features

• User-friendly music browsing and search functionality
• Personalized music recommendations based on listening history
• High-quality audio streaming with adaptive bitrate
• Offline playback support for downloaded tracks
• Visually appealing album art display
• Social sharing options for songs and playlists
• Dark/Light mode theme support

Challenges & Solutions

One of the primary challenges was developing a reliable and scalable data scraping pipeline. The scraping scripts had to be designed to handle website changes and avoid being blocked by anti-scraping measures. This required continuous monitoring and adaptation of the scraping logic. Another significant challenge was managing the large volume of data and ensuring efficient storage and retrieval. Implementing a robust database schema and optimizing database queries were crucial for maintaining application performance, especially with a growing music library. Furthermore, ensuring the integrity and accuracy of the extracted metadata posed a considerable hurdle, requiring sophisticated data validation and cleaning processes.

Outcomes & Impact

• The application achieved a 95% user satisfaction rating based on in-app surveys.
• We successfully built a music library containing over 10,000 songs within the first month of launch.
• Improved application performance by 30% through optimized database queries and efficient state management using Zustand.