👋 I’m Muzzamil Nagda, a Data Engineer with a passion for transforming raw data into actionable insights through efficient ETL pipelines and cloud technologies.

🚀 ETL Projects: I have practical experience in developing scalable ETL solutions using tools like Azure Data Factory, Databricks, and Synapse Analytics. My projects include cloud migrations, data quality management, and automation of reporting pipelines. Check out the repositories below to explore more!

🔧 Tech Stack:

• Cloud Platforms: Azure, Microsoft Fabric
• Languages & Tools: PySpark, SQL, Power BI
• Data Management: Lakehouse architecture, Blob Storage, Datalake Gen2

💬 Feel free to reach out if you want to collaborate, discuss data engineering best practices, or simply talk about innovative data solutions.

This project streamlines the process of gathering and analyzing the latest news by utilizing the Bing Web Search API in combination with Microsoft Fabric. Designed to collect fresh news daily, it applies sentiment analysis to each article and visualizes the results through a Power BI dashboard. Built with Microsoft Fabric tools like Data Factory, Lakehouse, Jupyter Notebooks, and Power BI, the pipeline provides efficient data orchestration, processing, and visualization. Configured alerts signal the arrival of new data, supporting prompt reviews and insights.

This project explores the Tokyo Olympics data, leveraging Azure’s powerful data engineering services to perform end-to-end data processing and analysis. Following Darshil Parmar's tutorial, the project covers data ingestion, transformation, and storage in a scalable pipeline. It uses Azure Data Factory for orchestration, Azure Databricks for data transformation, Azure Data Lake Storage for scalable storage, and Azure Synapse Analytics for data warehousing and analysis. Visual insights are generated through Power BI, creating a comprehensive data workflow that offers insights into Olympic performance, trends, and statistics.