Generate synthetic Spotify music stream dataset to create dashboards. Spotify API generates fake event data emitted to Kafka. Spark consumes and processes Kafka data, saving it to the Datalake. Airflow orchestrates the pipeline. dbt moves data to Snowflake, transforms it, and creates dashboards.

Wavvy: Podcast Hosting and Analytics software. Built on the PERN stack and hosted on Heroku. Uses AWS for image and audio file storage. Generates an RSS feed for the user to use on platforms such as iTunes and Spotify. Also gives users a customizable one page website with all their streaming and social links.

Interactive Spotify analytics dashboard visualizing top tracks, artist performance, streaming trends, and energy levels using Power BI. Highlights include average streams, song comparisons, and historical song count.

UTA senior design project of Patrick Arzoumanian, Gustavo Chavez, Abraham Mookhoek, Ahmed Ullah, and Spencer Whitehead. A music analytics web app that empowers users with insight into their Spotify streaming history.

Colorflow Player is a Node.js music streaming app (Spotify and Deezer) with a ColorFlow inspired UI and split-testing analytics.

🎵 Music Streaming System (Melodify) A full-stack music streaming platform inspired by Spotify, built using Spring Boot, React (Vite), and MS SQL Server. The application enables users to stream songs, create playlists, follow artists, and view analytics, while offering secure role-based access for Users, Artists, and Admins.

A music streaming app with HTML, CSS, JavaScript, and Spotify API integration. It offers genre collections, advanced search, a high-quality player, lyrics, and a playlist system for saving and liking songs. Includes analytics for liked songs and user listening habits.

A sophisticated full-stack music streaming platform inspired by Spotify, built with the MERN stack. This application delivers an immersive audio experience with real-time social features, comprehensive analytics, and a powerful admin dashboard for content management.

A music data tool for retrieving data from Spotify including streams for analytics.

A complete DevOps-driven data pipeline simulating Spotify streaming analytics — built with Docker, Kubernetes, GitHub Actions, and Python for ETL, monitoring, and visualization.

Streaming Analytics on Spotify Hot 200

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Python data analytics pipeline analyzing Spotify streaming trends using Pandas, SQL-style analysis, Matplotlib, Plotly dashboards, and Fernet encryption.

The Spotify Data Analysis Project demonstrates the power of data analysis in fields like business, research, and meteorology. Using Python, I analyzed music datasets to extract insights, visualizing data and discovering patterns. This project enhanced my skills and understanding of how data connects to music, preparing me for future endeavors.

SB19 Spotify Analytics Dashboard - Track streaming performance for SB19 and solo members

A music data tool for retrieving data from Spotify including streams for analytics.

Power BI dashboard for Spotify streaming analytics. Artist performance, track popularity trends, and content analysis with Spotify-themed UI.

Power BI dashboard exploring Spotify streaming trends — top artists, songs, and audio feature analytics.

Python project to pull data from Spotify, YouTube, and other music streaming platforms for analytics purposes

A real-time streaming analytics dashboard using Python and Tableau to monitor audience engagement and analyze trends in streaming volumes across Spotify and YouTube.

A comprehensive data analytics project leveraging Power BI and a Spotify dataset to visualize and analyze music streaming patterns, artist performance, and audio characteristics.

Spotify BigData Streaming is a real-time data streaming and analytics pipeline that processes event data using Kafka, Spark, and Hadoop HDFS. It follows a Star Schema approach, transforming raw data into structured formats with dbt and storing business-ready insights in ClickHouse. Finally, Metabase provides interactive visualizations for analytics

Developed an interactive Spotify Music Analytics Dashboard using Power BI to analyze global streaming trends and track performance. Visualized total streams, audio features, artist popularity, release year trends, and musical key distribution with dynamic filters for easy exploration and clear data insights.

🎧 Real-time Spotify streaming pipeline using Kafka, MinIO, Snowflake, DBT, Airflow, and Power BI. Simulates user events, stores raw data in cloud object storage, transforms it into analytics-ready models, and visualizes engagement insights through interactive dashboards.

Engineered the streaming crawler pipeline using Kafka to extract, transform, and load Spotify data into PostgreSQL and ClickHouse for real-time analytics. Additionally, developed an automated batching pipeline using Airflow and Spark to efficiently ETL crawled data into BigQuery.

Spotify data analysis involves creating a comprehensive table to store track, artist, and streaming information, followed by a series of exploratory data analysis (EDA) and analytical SQL queries to extract insights from the dataset.

This Power BI dashboard project dives deep into Spotify's music data to uncover trends, patterns, and insights across genres, artists, and listener behavior. Whether you're a data enthusiast, music lover, or aspiring analyst, this project showcases how data visualization can bring streaming analytics to life.

Análise de sucesso musical no Spotify para gravadora. Projeto de Data Analytics que valida hipóteses sobre streams vs. playlists, catálogo de artistas e correlação multiplataforma. Utiliza Google BigQuery para ETL e Power BI para dashboard interativo. Fornece insights e recomendações estratégicas para o crescimento de artistas.

This repository houses an implementation of a Spotify-esque streaming service and recommendation system utilizing Apache Spark and Kafka. It leverages a 100GB dataset of various mp3 files from the Free Music Archive. This was developed as part of a project for the course Fundamentals of Big Data Analytics (DS2004).

This project focuses on advanced SQL analysis using a real-world Spotify dataset. It covers end-to-end data exploration, schema design, and analytical querying to extract meaningful insights from music streaming data. The project includes exploratory data analysis, aggregation-based queries, subqueries, Common Table Expressions (CTEs), and window