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.

Ticketmaster Analytics Pipeline - Music Event Demand Timing Analysis using Airflow, dbt, and Snowflake

A data pipeline analyzing trends in music data from the Spotify API. Snowflake and Databricks integration.

Built an automated daily pipeline to fetch Spotify’s “Top 100 Global” playlist data using Python, store raw files in AWS S3, and transform them into structured Albums, Artists, and Songs tables; leveraged Snowpipe for near real-time ingestion into Snowflake.

Building a Seamless Data Pipeline with AWS and Snowpipe

Airflow DAG pipeline for extracting and transforming YouTube music data by country and region, with automated load to a MySQL-based snowflake schema.

This project is a cloud-native ETL pipeline that automates the flow of music data from the Spotify API into a Snowflake data warehouse for analytics.

A full-stack music archiving app built with Django, TypeScript, and Docker — featuring a Selenium scraping layer for streaming platforms and a Snowflake/dbt/Airflow analytics pipeline following medallion architecture.

This project is a complete ETL (Extract, Transform, Load) pipeline that leverages Spotify's Web API, AWS services, and Snowflake to automate the flow of music data from source to data warehouse.

End-to-end ETL pipeline using Azure Data Factory to migrate Chinook music data from Azure SQL Database → Azure Blob Storage (Parquet) → Snowflake Data Warehouse. Features dimensional modeling, incremental loading, data quality controls, and automated transformations with audit trails.

An end-to-end data pipeline that ingests music data from the Spotify API, streams it via Kafka, stores raw JSON in Amazon S3, and loads everything into Snowflake for transformation with dbt—ultimately delivering a star-schema model for analytics and dashboards.

This project builds a scalable, fault-tolerant real-time data pipeline simulating music streaming events. It integrates Apache Kafka for streaming ingestion, Apache Spark Structured Streaming for processing, Google Cloud Platform services for orchestration, and Snowflake as the cloud data warehouse for analytics.

Built an end-to-end real-time Spotify data analytics pipeline using Python, Kafka, MinIO, Apache Airflow, Snowflake, dbt, and Power BI. The project follows Medallion Architecture (Bronze, Silver, Gold) to ingest, transform, and visualize streaming music data for near real-time insights and dashboarding.