Azure Data Engineering Real-Time Experience Program

About this course

Turn your skills into hands-on project practice if you lack project exposure in Azure and Databricks, find data engineering interviews difficult, or want more confidence in PySpark and pipeline design.

Prerequisites: Ask an expert whether this program suits your background.

8

Learning units

47

Topics covered

4

Projects

12

Tools

1. 1

   Module 1

   Data Engineering Fundamentals

   • Data engineering roles and responsibilities
   • Azure ecosystem: ADF, ADLS, Databricks and Synapse
   • End-to-end data pipeline architecture
   • Roadmap to become a Data Engineer
2. 2

   Module 2

   Scenario-Based Interview Training and Hands-On Practice

   • Interview scenarios
   • SQL for data engineering
   • Python fundamentals for pipelines
   • PySpark transformations and logic
   • Databricks-based assignments
   • Problem-solving approach for interviews
3. 3

   Module 3

   Databricks Performance Optimization and Spark Internals

   • How Spark executes jobs (lazy evaluation)
   • Query execution plans and debugging
   • Caching and memory optimization
   • Data shuffling and partitioning strategies
   • Broadcast joins and optimization techniques
   • Z-ordering, data skipping and liquid clustering
   • Automating optimization tasks
4. 4

   Module 4

   Azure Databricks End-to-End Capstone Projects

   • Project A (Healthcare): Medallion Architecture
   • Project B (Banking): high-volume data pipeline
   • Projects C and D: team-executed projects
   • Delta Lake concepts and merge operations
   • CI/CD pipeline using Git
   • Integration with Azure Synapse / Fabric
   • RAG model capstone
5. 5

   Module 5

   Advanced Real-Time Data Pipeline Implementation

   • Building production-ready pipelines
   • Data profiling and validation
   • Monitoring and alerting
   • Error handling and retry strategies
   • GenAI integration in data pipelines
6. 6

   Module 6

   Real-Time Project Simulation and Workplace Readiness

   • Simulated company work environment
   • What to expect in your first 30–60 days on a job
   • Knowledge Transfer (KT) sessions
   • Requirement gathering techniques
   • Daily status tracking and reporting
   • Team collaboration and agile workflow
7. 7

   Module 7

   AI-Enabled Development and Productivity

   • AI in data engineering
   • Prompt engineering with Databricks Genie
   • AI tools for PySpark and SQL
   • Debugging and optimizing pipelines with AI
   • Accelerating project delivery with AI
8. 8

   Module 8

   Interview Strategy for Azure Data Engineering

   • Topic-wise questions: SQL, PySpark, Azure and Databricks
   • Scenario-based questions
   • How to explain your projects
   • Understanding production and development responsibilities
   • Resume building with 1:1 guidance
   • Mock interviews
   • Confidence building and answer structuring

Projects you will build

These are training projects. Please describe them honestly as training work on your resume and in interviews.

Tools and technologies

• Azure
• Databricks
• PySpark
• SQL
• Python
• Delta Lake
• ADF
• ADLS
• Synapse
• Fabric
• Git
• Databricks Genie