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A Data Engineering deep dive with several data warehouses using real data sourced from Kaggle

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SPM Superstore Data Engineering Project 🚀

As a data professional, I've always been fascinated by building robust data systems from the ground up. This project represents my journey to master modern data engineering while keeping my analytics skills sharp. Using the Olist e-commerce dataset, I'm developing an end-to-end data platform that challenges me to think both as an engineer and analyst. It's a passion project that combines my love for clean data architecture with the thrill of discovering insights through visualization.

Tech Stack

  • Cloud Platforms: Google Cloud Platform, Azure Cloud (data lake)
  • Languages & Tools: Python, SQL: Google BigQuery, Azuree
  • Visualization: Tableau, Power BI

Project Goals

  1. Build data warehouse structure in GCP and Azure with a series of ecommerce and retail sales data tables
  2. Implement data modeling and transformations using Python and dbt
  3. Create analytics dashboards in Tableau and Power BI
  4. Practice modern data engineering practices and cloud architecture
  5. Document development process and technical decisions

🔍 Project demonstrates practical implementation of modern data engineering and analytics practices.

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A Data Engineering deep dive with several data warehouses using real data sourced from Kaggle

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