PROJECTS
Tech Stack:
Google Cloud Storage, Google Compute Engine, Mage ETL, Python (Pandas), BigQuery, Looker Studio, Star Schema Modeling
​This project transforms NYC taxi data into actionable insights through a cloud-based data pipeline on Google Cloud Platform. Raw trip records flow through Mage ETL, where Python transformations create a structured star schema optimizing query performance in BigQuery. Interactive Looker Studio dashboards visualize 100K trips totaling $1.64M, revealing key patterns like higher Nassau/Westchester trip fares and increased tips from credit card payments. The geospatial heat maps and comparative charts enable transportation pattern analysis across vendors, payment types, and NYC locations.
Tech Stack:
AWS (Lambda, S3, Glue, Athena) Tableau, Python, Spotipy API, JSON, Extract, Transform, Load (ETL)
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This project unlocks music trends from Spotify playlists through an automated AWS-powered pipeline. The serverless architecture harvests weekly data from playlists like Anjunadeep 2024, transforming raw track metadata into structured analytics via Lambda functions and S3. Interactive Tableau dashboards reveal artist popularity patterns, track performance metrics, and album statistics, providing valuable insights for playlist curation and music trend analysis.
Tech Stack:
Alteryx, SQL Server, ER Studio, Power BI, Tableau, Dbeaver
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This project transforms NYC's restaurant inspection data into a comprehensive food safety analytics system through a multi-stage ETL pipeline. Raw Department of Health records flow through Alteryx workflows for cleansing before populating a star schema model with 9 dimension tables in SQL Server. Interactive dashboards visualize 94,000+ inspections across NYC's five boroughs, revealing violation hotspots and cuisine-specific compliance patterns that enable health officials to identify high-risk establishments and strategically allocate inspection resources to prevent potential foodborne illness outbreaks.
Tech Stack:
MySQL · Microsoft Power BI · Microsoft Excel · DAX · Power Query​
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This project harnesses an automated ETL pipeline to process over 100K monthly sales transactions from MySQL, enhancing data accuracy and reducing manual labor. The integration with Power BI provides a dynamic dashboard that displays real-time insights into revenue trends and market performance, significantly speeding up decision-making processes for the sales team.
