What will you learn in Microsoft Azure Data Engineering Training Course
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 with Azure Data Factory, Synapse Pipelines, and Azure Databricks
Apply real-time processing using Azure Stream Analytics and Event Hubs for streaming data scenarios
Secure data solutions with role-based access, managed identities, encryption, and monitoring best practices
Program Overview
Module 1: Introduction to Azure Data Engineering & DP-203
⏳ 2 hours
Topics: Azure data services landscape, DP-203 exam objectives, course setup
Hands-on: Provision a free-tier Azure account and run your first Data Factory pipeline
Module 2: Data Storage Solutions
⏳ 6 hours
Topics: Data Lake Storage Gen2 architecture, Blob storage tiers, Synapse SQL pools
Hands-on: Create storage accounts, ingest sample data, and query with serverless SQL
Module 3: Data Ingestion & Integration
⏳ 8 hours
Topics: Azure Data Factory pipelines, mapping data flows, connectivity options
Hands-on: Build and schedule ETL pipelines integrating multiple data sources
Module 4: Batch & Real-Time Processing
⏳ 6 hours
Topics: Azure Databricks notebooks, Spark fundamentals, Delta Lake; Stream Analytics jobs and Event Hubs
Hands-on: Develop a Spark job for data transformation and configure a streaming query
Module 5: Data Warehousing with Synapse
⏳ 6 hours
Topics: Dedicated vs. serverless SQL pools, distribution strategies, workload management
Hands-on: Design a star schema in Synapse and optimize query performance
Module 6: Security, Governance & Monitoring
⏳ 4 hours
Topics: RBAC, managed identities, encryption at rest/in transit; Azure Monitor, Log Analytics
Hands-on: Configure role assignments, set up diagnostic logs, and create alert rules
Module 7: Capstone Project & Exam Prep
⏳ 4 hours
Topics: DP-203 exam blueprint review, sample questions, best-practice patterns
Hands-on: Implement an end-to-end data solution—ingest, process, secure, and query—and walk through a practice exam
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Job Outlook
In the U.S., Azure Data Engineers earn a median salary of $120,000 per year
In India, professionals command around ₹10–14 LPA depending on experience and location
Strong demand across finance, healthcare, retail, and tech as organizations adopt cloud-native analytics
Roles include Data Engineer, Analytics Engineer, and Cloud Solutions Architect, with growth driven by big data and AI initiatives
Explore More Learning Paths
Take your data engineering and cloud analytics skills to the next level with these hand-picked programs designed to deepen your expertise and advance your career in data management and cloud technologies.
Related Courses
IBM Data Engineering Professional Certificate Course – Gain hands-on experience with data pipelines, ETL processes, and cloud-based data engineering tools.
Data Engineering, Big Data, and Machine Learning on GCP Specialization Course – Learn to process large datasets and integrate machine learning workflows on Google Cloud Platform.
Data Engineering Foundations Specialization Course – Build a solid foundation in data modeling, pipeline design, and scalable data solutions.
Related Reading
What Is Data Management? – Understand how effective data management practices are critical for robust data engineering and accurate analytics.
Specification: Microsoft Azure Data Engineering Training Course
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