Azure Data Engineering

Project Overview

The Azure Data Engineering project demonstrates the implementation of a modern data engineering pipeline using Microsoft's Azure ecosystem and other technologies. The pipeline is designed to process, transform, and visualize data efficiently while adhering to the Medallion Architecture for a structured approach to data lake organization.

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Tech Stack Used

1. SQL Server (On-Premises):

   • Acts as the source system for data extraction.

2. Azure SQL Server:

   • Cloud-hosted relational database for storing structured, transformed data.

3. Azure Blob Storage:

   • Serves as a scalable data lake for raw and intermediate data storage.

4. Azure Data Factory (ADF):

   • Orchestrates ETL/ELT pipelines to extract, transform, and load data between various systems.

5. Azure Databricks:

   • Used for large-scale data processing and transformations, particularly for complex data engineering workflows.

6. Power BI:

   • Business intelligence tool for creating interactive dashboards and visualizations from the processed data.

7. Medallion Architecture:

   • Ensures the pipeline adheres to a structured framework with three distinct layers:
     • Bronze Layer: Raw data ingestion.
     • Silver Layer: Cleansed and enriched data.
     • Gold Layer: Aggregated and analytics-ready data.

8. RTL (Real-Time Layer):

   • Enables real-time processing for use cases requiring low-latency data insights.

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Key Features

• End-to-End Data Pipeline:

  • Extracts data from on-premises SQL Server and ingests it into Azure Blob Storage using Azure Data Factory.
  • Transforms raw data into analytics-ready formats using Databricks and ADF.
  • Stores structured data in Azure SQL Server for downstream consumption.

• Scalable Data Architecture:

  • Implements the Medallion Architecture to ensure scalability, reusability, and maintainability of data.

• Real-Time Processing:

  • Includes a real-time processing layer for use cases such as streaming data analytics or real-time dashboards.

• Interactive Dashboards:

  • Provides actionable insights through Power BI reports and dashboards.

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Project Workflow

1. Data Ingestion:

   • Extract raw data from on-premises SQL Server.
   • Load raw data into the Bronze Layer (Blob Storage).

2. Data Transformation:

   • Process data in Databricks to clean and enrich it (Silver Layer).
   • Perform advanced transformations for analytical purposes (Gold Layer).

3. Data Storage:

   • Store transformed data in Azure SQL Server for easy querying and integration with BI tools.

4. Data Visualization:

   • Develop Power BI dashboards for visualizing KPIs and insights.

5. Real-Time Processing:

   • Incorporate streaming data pipelines for real-time analytics when needed.

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Directory Structure

azure-data-engineering/
├── data_ingestion/
│   ├── adf_pipelines/
│   └── blob_storage/
├── data_transformation/
│   ├── databricks_notebooks/
│   └── sql_scripts/
├── data_visualization/
│   └── powerbi_dashboards/
├── docs/
│   └── architecture_diagram.png
└── README.md

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Prerequisites

1. Azure account with the following services set up:

   • Azure SQL Server
   • Azure Blob Storage
   • Azure Data Factory
   • Azure Databricks

2. On-premises SQL Server access with relevant credentials.

3. Power BI Desktop for visualization development.

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How to Run the Project

1. Setup Azure Services:

   • Configure Azure SQL Server, Blob Storage, and Data Factory.
   • Set up Databricks workspace and clusters.

2. Deploy Pipelines:

   • Import ADF pipelines into Azure Data Factory.
   • Create Databricks notebooks for transformations.

3. Ingest Data:

   • Use ADF to extract data from on-premises SQL Server to Blob Storage.

4. Transform Data:

   • Run Databricks notebooks to process and transform data through Bronze, Silver, and Gold layers.

5. Visualize Data:

   • Connect Power BI to Azure SQL Server or Gold Layer outputs and build dashboards.

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Architecture Diagram

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Future Enhancements

1. Add support for additional data sources (e.g., APIs, third-party databases).
2. Implement CI/CD for automated pipeline deployments.
3. Optimize real-time processing with Azure Event Hub and Stream Analytics.
4. Introduce advanced analytics with ML models in Databricks.

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Contributing

Feel free to fork this repository, submit pull requests, or create issues for suggestions and improvements.

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