🎓 Azure Data Engineering Course Syllabus

🧑‍🏫 Target Audience:

• Aspiring Data Engineers

• ETL Developers

• Data Analysts transitioning to engineering

• Cloud Engineers focusing on Azure

📅 Total Duration: 8 to 10 Weeks

📍 Mode: Online | Practical + Theory Mix | Use Case Driven

🧩 Module 1: Introduction to Azure & Data Engineering 

• Overview of Cloud Computing & Azure

• Introduction to Data Engineering

• Understanding Azure Portal & Resources

• Azure Regions, Subscriptions, Resource Groups

• Azure Storage Options: Blob, Data Lake Gen2

🕒 Duration: 3–4 hours

🧩 Module 2: Azure Storage Fundamentals 

• Azure Blob Storage, ADLS Gen2

• Hierarchical Namespace

• Access Tiers and Security

• Hands-on with Storage Explorer, SAS, RBAC

🕒 Duration: 4–6 hours

🧩 Module 3: Azure Data Factory (ADF)

• ADF Architecture and Components

• Linked Services, Datasets, Pipelines

• Copy Activity

• Triggers, Parameters, Variables

• Monitor & Debug Pipelines

• Integration Runtimes

• Best Practices for ADF Pipelines

🕒 Duration: 10–12 hours

🧩 Module 4: Azure SQL & Synapse Analytics

• Azure SQL Database vs Managed Instance

• Synapse Architecture Overview

• Dedicated vs Serverless Pools

• Writing T-SQL Queries

• External Tables, Views

• Data Loading via PolyBase

🕒 Duration: 8–10 hours

🧩 Module 5: Azure Data Lake + Data Integration 

• ADLS Gen2 Structure & Access Control

• Mounting & Reading Data

• Delta Lake Concepts

• Storing Raw → Refined → Curated Zones

🕒 Duration: 6–8 hours

🧩 Module 6: Azure Databricks & PySpark 

• What is Databricks? Architecture

• Cluster Types (Interactive, Job)

• Introduction to Notebooks, DBFS

• PySpark Basics (RDD vs DataFrames)

• Transformations & Actions

• Delta Lake, Optimization (Z-Ordering, Caching)

• Writing Structured Streaming Jobs

🕒 Duration: 12–14 hours

🧩 Module 7: Orchestration, Monitoring & CI/CD

• ADF + Databricks Integration

• Log Analytics, Alerts

• Azure Monitor

• Azure DevOps CI/CD for Data Pipelines

• ARM Templates, Key Vault Integration

🕒 Duration: 8–10 hours

🧩 Module 8: Real-Time Data Processing 

• Real-time Data Pipeline Use Case

🕒 Duration: 4–5 hours

🧩 Module 9: Capstone Project

• End-to-End Project:

  • Ingest raw data from Blob/SQL

  • Transform using Databricks

  • Code Review + Best Practices

🕒 Duration: 10 hours

💡 Tools Covered

• Azure Portal

• Azure Data Factory

• Azure SQL DB / Synapse Analytics

• Azure Storage Explorer

• Azure Key Vault

• Azure DevOps

• Azure Databricks

🧾 Add-ons (If Required)

• DP-203 Certification Prep

• Resume / LinkedIn Optimization for Data Engineers

• Mock Interviews