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mage-ai

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This end-to-end data pipeline project processes and analyzes Maintenance Work Orders using Mage, Docker, Google BigQuery, MariaDB, and Looker Studio. It features a seamless integration of cloud and open-source tools for scalable data storage, transformation, and visualization.

  • Updated Nov 5, 2024
  • Jupyter Notebook

This project showcases a data engineering pipeline using Google Cloud Platform (GCP) for analyzing taxi trips data from New York City. The pipeline includes steps such as data collection, storage setup, ETL processing, database creation, and data visualization.

  • Updated Mar 5, 2024
  • Jupyter Notebook

Developed an interactive dashboard utilizing Google Looker Studio to analyze Uber usage patterns in NYC. Designed a robust data model and implemented an efficient data pipeline using Mage AI on GCP's VM infrastructure. Utilized Google BigQuery for in-depth analytics and leveraged Google Cloud Platform for data processing.

  • Updated Feb 2, 2024
  • Jupyter Notebook

Engineered a robust data pipeline using Google Cloud Platform (GCP) services like Cloud Storage, Compute Engine, BigQuery, resulting in a 30% decrease in data processing and analysis time of the San Francisco Crime dataset.Designed an interactive dashboard using Looker Studio, empowering stakeholders to explore crime data visually.

  • Updated Jan 20, 2024
  • Jupyter Notebook

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