What will you learn in Microsoft Azure Data Engineering Training Course
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Design and implement scalable data storage solutions using Azure Data Lake Storage Gen2, Blob Storage, and Synapse Analytics
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Build end-to-end ETL/ELT pipelines with Azure Data Factory, Synapse Pipelines, and Azure Databricks
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Apply real-time processing using Azure Stream Analytics and Event Hubs for streaming data scenarios
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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
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Topics: Azure data services landscape, DP-203 exam objectives, course setup
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Hands-on: Provision a free-tier Azure account and run your first Data Factory pipeline
Module 2: Data Storage Solutions
⏳ 6 hours
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Topics: Data Lake Storage Gen2 architecture, Blob storage tiers, Synapse SQL pools
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Hands-on: Create storage accounts, ingest sample data, and query with serverless SQL
Module 3: Data Ingestion & Integration
⏳ 8 hours
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Topics: Azure Data Factory pipelines, mapping data flows, connectivity options
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Hands-on: Build and schedule ETL pipelines integrating multiple data sources
Module 4: Batch & Real-Time Processing
⏳ 6 hours
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Topics: Azure Databricks notebooks, Spark fundamentals, Delta Lake; Stream Analytics jobs and Event Hubs
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Hands-on: Develop a Spark job for data transformation and configure a streaming query
Module 5: Data Warehousing with Synapse
⏳ 6 hours
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Topics: Dedicated vs. serverless SQL pools, distribution strategies, workload management
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Hands-on: Design a star schema in Synapse and optimize query performance
Module 6: Security, Governance & Monitoring
⏳ 4 hours
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Topics: RBAC, managed identities, encryption at rest/in transit; Azure Monitor, Log Analytics
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Hands-on: Configure role assignments, set up diagnostic logs, and create alert rules
Module 7: Capstone Project & Exam Prep
⏳ 4 hours
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Topics: DP-203 exam blueprint review, sample questions, best-practice patterns
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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
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In the U.S., Azure Data Engineers earn a median salary of $120,000 per year
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In India, professionals command around ₹10–14 LPA depending on experience and location
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Strong demand across finance, healthcare, retail, and tech as organizations adopt cloud-native analytics
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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
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IBM Data Engineering Professional Certificate Course – Gain hands-on experience with data pipelines, ETL processes, and cloud-based data engineering tools.
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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.
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Data Engineering Foundations Specialization Course – Build a solid foundation in data modeling, pipeline design, and scalable data solutions.
Related Reading
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What Is Data Management? – Understand how effective data management practices are critical for robust data engineering and accurate analytics.
