data-bricks

End-to-end data engineering pipeline using Medallion Architecture (Bronze→Silver→Validation→Gold) on Databricks with PySpark, Apache Airflow orchestration and Power BI dashboard

About This project focuses on performing an end-to-end analysis of IPL data using Apache Spark on Databricks. It begins with setting up a Databricks environment, followed by ingesting and exploring the IPL dataset.

AI-Powered Movie Recommendation System on Databricks.

End-to-end data pipeline that ingests 12 Transfermarkt football datasets (1.9M+ records) into Azure Data Lake, transforms them through a medallion architecture in Databricks Unity Catalog, and serves analyst-ready KPIs to Power BI.

Azure End To End Data Enginnering Project

🚀 Production-grade sales analytics on Databricks | Medallion Architecture (Bronze→Silver→Gold) | AI-powered dashboards | 97% Genie AI accuracy | Unity Catalog governance | 790 lines of SQL/Python

use gg colab cleaning data and training, compare and choose the suitable algorithms. Then, create app integrating machine learning and genAI. This app use to recommend top 3 suitable trees should to plant. This projects also has been supported by AI (chatgpt, copilot)

End-to-end medallion architecture pipeline on Databricks processing 19.4M NYC Yellow Taxi records through Bronze, Silver, and Gold Delta Lake layers. Covers Unity Catalog, schema evolution handling, OPTIMIZE/ZORDER, and 4 Gold aggregation tables.

Movie market trend analysis using Apache PySpark and Databricks — revenue patterns, genre performance, and ratings across 1000 IMDB films (2006–2016)