Azure Data Engineering End-to-End Solution

Overview

This repository contains a comprehensive end-to-end Azure Data Engineering solution, covering the entire data lifecycle from ingestion to reporting. The project utilizes various Azure services and tools to achieve efficient data handling, transformation, and reporting. This project is useful for beginner Data engineer and anyone who want to upskill and get hands-on experience on Azure Data Engineering.

Project Components

1. Azure Data Factory

• Purpose: Data ingestion from on-premise SQL Server and orchestration of data transformation processes.
• Features:
  • Ingestion of raw data from on-premise SQL Server.
  • Orchestration of Azure Databricks notebooks for data transformations.
  • Pipeline scheduling and monitoring.

2. Azure Data Lake Storage Gen2

• Purpose: Storage solution for data across different stages (Bronze, Silver, Gold).
• Layers:
  • Bronze: Raw ingested data.
  • Silver: Transformed data in delta format.
  • Gold: Aggregated data in delta format.

3. Azure Databricks

• Purpose: Data transformation from Bronze to Silver and from Silver to Gold layers.
• Features:
  • Mounting Azure Data Lake Storage using configuration notebooks.
  • Executing data transformation logic using Databricks notebooks.

4. Azure Synapse Analytics

• Purpose: Loading data from the Gold layer into a serverless SQL pool.
• Activities:
  • Creation of views using stored procedures.
  • Data integration and querying capabilities.

5. Azure Key Vault

• Purpose: Secure storage for credentials and tokens.
• Usage:
  • Storing credentials for on-premise SQL Server (source system).
  • Managing Personal Access Tokens (PATs) for Azure Databricks.

6. Azure Active Directory (AAD)

• Purpose: Security and access control management.
• Features:
  • Ensuring secure access to Azure services and resources.
  • Integration with Azure Key Vault for governance.

7. Microsoft Power BI

• Purpose: Creation of interactive reports and dashboards.
• Reports:
  • Customer-Products Analysis.
  • Sales Data Analysis.
• Integration: Connecting to Azure Synapse SQL Server for data visualization.

Use Case

The project's objective is to build an end-to-end solution for data processing and reporting:

• Data Ingestion: Retrieve tables from on-premise SQL Server using Azure Data Factory.
• Data Storage: Store raw and transformed data in Azure Data Lake Storage Gen2.
• Data Transformation: Use Azure Databricks to clean and transform raw data into structured formats.
• Data Loading: Load transformed data into Azure Synapse Analytics using serverless SQL pools.
• Reporting: Utilize Microsoft Power BI to create insightful reports and dashboards.
• Governance and Security: Implement Azure Key Vault for secure credential storage and Azure Active Directory for access control.

Project Structure

• /ADF_Pipelines: Contains Azure Data Factory pipeline definitions.
• /Databricks_Notebooks: Includes Databricks notebooks for data transformation tasks.
• /SQL_Scripts: Scripts for Azure Synapse Analytics (SQL pool views, stored procedures).
• /PowerBI_Reports: Power BI reports for Customer-Products analysis and Sales data analysis.

Setup Instructions

1. Azure Services Setup: Deploy necessary Azure services (Data Factory, Data Lake Storage, Databricks, Synapse Analytics, Key Vault, AAD). Project can implemented using Azure Free trial (30 days) and Azure Databricks trial (14 days).

2. Configuration:

   • Configure Azure Data Factory pipelines for data ingestion and orchestration.
   • Set up Azure Databricks clusters and mount Azure Data Lake Storage.
   • Define views and stored procedures in Azure Synapse Analytics.
   • Configure Power BI to connect with Azure Synapse SQL Server.

3. Security Setup:

   • Populate Azure Key Vault with required credentials and tokens.
   • Configure Azure Active Directory for user roles and permissions.

4. Execution:

   • Install MS-SQL Server express version on local computer with AdventureWorksLT2022 database.
   • Deploy all the required azure services.
   • Mount ADLS Gen2 storage to databricks using config notebook (required only once).
   • Follow steps to create ADF piplelines for data ingestion (copy all tables)
   • Create databicks notebook for transformations (bronze to silver and silver to gold)
   • Add both the databricks notebooks activity in ADF pipeline.
   • Create views using Azure Synapse Analytics under SQL Serverless pool, stored prcoedure needs to be created here.
   • Run ADF pipeline to load data from source till gold layer, then run synapse prcoedure.
   • Connect Power BI with Azure Syanpse SQL sever and import all the views.
   • Explore the tables,data model and create reports using different visuals.
   • Insert new data in customer table and rerun ADF pipeline using schedule trigger.
   • Refresh Power BI Dashboard to view the changes in reports.

By following these steps and utilizing the provided tools and scripts, you can successfully implement and test the end-to-end Azure Data Engineering project.

Support

• Feel free to reach out for any further assistance or clarification regarding the project setup and implementation. Email at [email protected]
• Happy data engineering!