A portable Datamart and Business Intelligence suite built with Docker, Mage, dbt, DuckDB and Superset
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Updated
Jul 20, 2024 - Dockerfile
A portable Datamart and Business Intelligence suite built with Docker, Mage, dbt, DuckDB and Superset
End-to-end data engineering project
An end-to-end data pipeline solution to process and analyze Maintenance Work Orders using Mage, Google BigQuery, Cloud SQL, and Looker Studio. Features a seamless integration of cloud tools for scalable data storage, transformation, and visualization.
Data modeling and ETL pipeline for data analytics on Uber dataset using Google cloud storage, BigQuery, and Looker Studio
A data engineering project built around Smogon's Stats API.
Solutions for @DataTalksClub's Data Engineering Zoomcamp 2024.
mlops zoomcamp capstone project
In this project, I demonstrate creating a data pipeline from NYC Taxi data using Mage.ai for ETL, GCP for storage and BigQuery, and Looker Studio for analytics.
Performed data analytics on Uber data using various tools and technologies, including GCP Storage, Python, Compute Instance, Mage Data Pipeline Tool, BigQuery, and Looker Studio.
An end-to-end data engineering project using Amazon S3, EC2, mage.io, Google BigQuery and Looker.
Personal project for Data Engineering Zoomcamp
The CNPJ Data ETL Pipeline is designed to automate the download, processing, and storage of public CNPJ data from the Brazilian Federal Revenue. The pipeline is built with Mage.ai and AWS S3 to ensure efficient data management and scalability.
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.
An end to end Data Engineering Project
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.
Data Engineering Zoomcamp 2024
A quick analysis of Yellow Taxi cabs riding in New York 2022
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