A cloud-native data pipeline and visualization project analyzing Formula 1 racing data using Azure, Databricks, Delta Lake, Tableau, and Python for insightful EDA and interactive dashboards.

End-to-end data pipeline transforming Olist e-commerce data through Azure cloud services. Implements medallion architecture (Bronze-Silver-Gold) with multi-source ingestion, Spark-based processing, and OLTP-to-OLAP optimization for analytics-ready datasets.

End-to-end backend and data hub architecture on Azure, integrating Databricks and a suite of Azure services for seamless data processing, analytics, and deployment.

Connecting to ADLS Gen 2 storage written for analysts.

In this project, I've created an end-to-end ETL pipeline and subsequently developed a machine learning model to predict the price of Amazon products based on several product-related features.

This Project involves building an e-commerce order data warehouse on Azure Synapse Analytic, leveraging the power of Azure Data Lake Storage Gen2, Synapse Pipelines, Data Flows, and Serverless SQL Pools.

This project demonstrates a complete ETL pipeline for Formula 1 racing data using Azure Databricks, Delta Lake, and Azure Data Factory. It covers data ingestion, transformation with PySpark and Spark SQL, data governance with Unity Catalog, and visualization through Power BI. Designed to showcase real-world data engineering workflows in Azure.

lake House Architecture for data transformation and migration using various Azure services