Introduction to Azure

Azure is Microsoft’s cloud computing platform and a leader in enterprise data solutions. For a Data Engineer, Azure provides a seamless ecosystem for storing, integrating, and managing massive data workloads using specialized tools like ADLS and Synapse.

🔗 Learning Resources

• Official Training: Azure Data Fundamentals (DP-900) - The best starting point for the Microsoft ecosystem.
• Storage Guide: Introduction to Azure Data Lake Storage (ADLS) Gen2
• Architecture Center: Azure Data Engineering Architecture

☁️ Azure Practice Tasks: Enterprise Data Engineering

Azure is the cloud of choice for many enterprise organizations. In this module, you will build a scalable data ingestion and transformation pipeline.

📝 Practice Tasks: Data Lake, Data Factory, and Synapse

Task 1: ADLS Gen2 Hierarchy

Goal: Master “Hierarchical Namespaces” for data organization.

• Requirement: Create an Azure Storage Account (Standard V2).
• Challenge: 1. Enable Hierarchical Namespace (this turns it into ADLS Gen2). 2. Create a “Container” named landing-zone. 3. Upload grocery_sales.csv using Azure Storage Explorer or the Portal.
• Concept: Understand why a “True Directory” structure is faster for Big Data than a “Flat” S3-style bucket.

Task 2: Azure Data Factory (ADF) - The Copy Activity

Goal: Your first “No-Code” ETL pipeline.

• Requirement: Provision an Azure Data Factory instance.
• Challenge: 1. Create a Linked Service to your Storage Account. 2. Create a Pipeline with a Copy Data Activity. 3. Source: grocery_sales.csv. Sink: A new folder named /processed/.
• Verification: Trigger the pipeline and check if the file moved successfully.

Task 3: Azure Key Vault Security

Goal: Secure your credentials (Secret Management).

• Requirement: Create an Azure Key Vault.
• Challenge: 1. Store your Storage Account Access Key as a Secret. 2. Link your Data Factory to the Key Vault. 3. Configure the Data Factory to pull the secret instead of hardcoding the password in the connection string.

🏗️ Mini Project: The “ADF-to-SQL” Pipeline

Build an end-to-end automated ingestion layer:

1. Storage: Landing zone in ADLS Gen2.
2. Orchestration: An Azure Data Factory pipeline triggered by a “Storage Event” (when a file is uploaded).
3. Transformation: A Mapping Data Flow in ADF that:
   • Removes rows with NULL prices.
   • Converts item_name to lowercase.
4. Loading: Sink the cleaned data into an Azure SQL Database table.
5. Monitoring: Use the ADF Monitor tab to verify the run time and rows processed.