This project is a comprehensive data engineering pipeline built on Microsoft Azure. The goal is to ingest, transform, and visualize data using cloud-based services such as Azure Data Factory, Data Lake, Databricks, Synapse Analytics, and Power BI.
The pipeline follows the Bronze-Silver-Gold architecture, ensuring data is cleaned, transformed, and optimized for analysis.
✅ Data Ingestion with Azure Data Factory (ADF) 🏗️
- Creating Azure Data Factory to automate data movement
- Setting up Linked Services for HTTP data sources and Azure Data Lake
- Building dynamic pipelines to process multiple datasets efficiently
✅ Data Storage with Azure Data Lake Storage Gen2 (ADLS) ☁️
- Creating three storage layers:
- Bronze (Raw data)
- Silver (Cleaned and structured data)
- Gold (Final optimized datasets for analysis)
✅ Data Transformation with Azure Databricks 🔥
- Using Apache Spark for scalable data processing
- Writing PySpark scripts to clean, enrich, and transform data
- Storing transformed data in the Gold layer
✅ Data Warehousing with Azure Synapse Analytics 🗄️
- Creating Synapse SQL pools to store processed data
- Designing tables and views for efficient querying and reporting
✅ Data Visualization with Power BI 📊
- Connecting Synapse Analytics to Power BI
- Building interactive dashboards for data analysis
- Publishing reports for stakeholder insights
🎯 Goal:
Build a scalable, cloud-native data engineering pipeline that automates data ingestion, transformation, storage, and visualization, enabling seamless analytics and business intelligence.