Microsoft Azure Data Engineering Training Course Syllabus

Overview: This comprehensive Microsoft Azure Data Engineering Training Course is designed for beginners aiming to master data engineering on Azure and prepare for the DP-203 certification exam. The course spans approximately 36 hours of hands-on learning delivered through live instructor-led sessions, 24×7 lab access, and real-world projects. You'll gain practical experience across core Azure data services, including Data Lake Storage, Data Factory, Databricks, Synapse Analytics, and Stream Analytics. With a project-driven approach, the curriculum aligns tightly with DP-203 exam objectives, culminating in a capstone project that integrates ingestion, processing, security, and monitoring. Lifetime access to recordings and materials ensures ongoing learning.

Module 1: Introduction to Azure Data Engineering & DP-203

Estimated time: 2 hours

• Azure data services landscape
• DP-203 exam objectives
• Course setup and tools
• Provisioning a free-tier Azure account
• Running your first Data Factory pipeline

Module 2: Data Storage Solutions

Estimated time: 6 hours

• Data Lake Storage Gen2 architecture
• Blob storage tiers and use cases
• Synapse SQL pools: serverless and dedicated
• Creating storage accounts and ingesting data
• Querying data with serverless SQL

Module 3: Data Ingestion & Integration

Estimated time: 8 hours

• Azure Data Factory pipelines
• Mapping data flows
• Connectivity options for hybrid and cloud sources
• Building ETL pipelines
• Scheduling and monitoring data integration workflows

Module 4: Batch & Real-Time Processing

Estimated time: 6 hours

• Azure Databricks notebooks and Spark fundamentals
• Delta Lake architecture and operations
• Azure Stream Analytics jobs
• Event Hubs for streaming data ingestion
• Developing a Spark job and configuring a streaming query

Module 5: Data Warehousing with Synapse

Estimated time: 6 hours

• Dedicated vs. serverless SQL pools
• Distribution strategies for performance
• Workload management in Synapse
• Designing a star schema
• Optimizing query performance

Module 6: Security, Governance & Monitoring

Estimated time: 4 hours

• Role-based access control (RBAC)
• Managed identities for secure access
• Data encryption at rest and in transit
• Azure Monitor and Log Analytics
• Setting up diagnostic logs and alert rules

Module 7: Capstone Project & Exam Prep

Estimated time: 4 hours

• DP-203 exam blueprint review
• Sample questions and test strategies
• Implementing an end-to-end data solution
• Ingesting, processing, securing, and querying data
• Practice exam walkthrough

Prerequisites

• Basic understanding of cloud computing concepts
• Familiarity with SQL and data modeling fundamentals
• Access to a computer with internet and ability to create an Azure free account

What You'll Be Able to Do After

• Design and implement scalable data storage solutions using Azure Data Lake Storage Gen2, Blob Storage, and Synapse Analytics
• Build end-to-end ETL/ELT pipelines using Azure Data Factory and Synapse Pipelines
• Process batch and streaming data with Azure Databricks and Stream Analytics
• Secure data platforms using RBAC, managed identities, and encryption
• Prepare for and pass the DP-203 certification exam