Azure Data Engineering Masterclass: ADF, Databricks, PySpark (Udemy.com)

Build Production-Ready Data Engineering Pipelines on Microsoft Azure Using Azure Data Factory, Databricks, PySpark, SQL, Delta Lake and Real-Time Industry Projects

Welcome to the Azure Data Engineering Masterclass, a comprehensive course designed to help you become a confident Azure Data Engineer by mastering the complete modern Azure Data Engineering ecosystem.

Whether you're a beginner looking to start your Data Engineering journey or an experienced professional preparing for interviews, certifications, or real-world projects, this course will provide everything you need—from the fundamentals to advanced production-ready implementations.

Unlike traditional Azure Data Factory courses that focus only on pipeline activities, this course teaches you how complete enterprise-grade data engineering solutions are built using multiple Azure services working together.

Every concept has been explained using practical examples, real-time business scenarios, and production-ready implementation techniques used by experienced Data Engineers.

Why This Course?

Modern Data Engineering is much more than creating Azure Data Factory pipelines.

In real-world projects, Data Engineers work with:

• Azure Data Factory

• Azure Databricks

• PySpark

• SQL

• Delta Lake

• Azure Data Lake Storage Gen2

• Azure Blob Storage

• Azure Key Vault

• REST APIs

• Data Warehousing

• Spark Architecture

• End-to-End ETL Pipelines

This course combines all these technologies into a single structured learning path.

Instead of learning isolated concepts, you'll understand how they work together in real enterprise projects.

What You'll Learn

Azure Data Factory (ADF)

• Build production-ready ETL and ELT pipelines

• Pipeline Activities

• Control Flow Activities

• Data Flows

• Parameterization

• Dynamic Content

• Variables

• Expressions

• Lookup Activity

• ForEach Activity

• Until Activity

• Switch Activity

• If Condition

• Metadata-driven Pipelines

• Incremental Data Loading

• Scheduling using Triggers

• Manual and Event Triggers

• Monitoring

• Debugging

• Logging

• Azure Monitor

• Log Analytics

• Pipeline Best Practices

• Naming Standards

• Production Deployment Techniques

SQL for Data Engineers

• SQL Fundamentals

• Joins

• Window Functions

• Common Table Expressions (CTEs)

• Stored Procedures

• Views

• Temporary Tables

• Performance Tips

• Real Interview Questions

Azure Databricks

• Databricks Workspace

• Clusters

• Notebooks

• Architecture

• Driver and Worker Nodes

• Jobs

• Workspace Management

PySpark

• DataFrames

• Reading and Writing Files

• Transformations

• Actions

• Joins

• Aggregations

• Window Functions

• UDFs

• Real-Time Data Processing

Spark Internals

• Spark Architecture

• Driver

• Executors

• Cluster Manager

• DAG

• Lazy Evaluation

• Transformations vs Actions

• Jobs

• Stages

• Tasks

• Partitioning

• Shuffle

• Performance Concepts

Delta Lake

• Delta Tables

• ACID Transactions

• Time Travel

• Schema Enforcement

• Schema Evolution

• MERGE

• UPDATE

• DELETE

• OPTIMIZE

• VACUUM

• Best Practices

Data Warehousing

• Fact Tables

• Dimension Tables

• Slowly Changing Dimensions (SCD)

• SCD Type 1

• SCD Type 2

• Incremental Loading

• Warehouse Design Concepts

Real-Time Data Engineering

• REST API Integration

• JSON Processing

• Azure Blob Storage

• Azure Data Lake Storage Gen2

• SQL Server Integration

• Self-Hosted Integration Runtime

• Dynamic File Processing

• Multiple Table Loading

• Email Notifications

• Error Handling

• Logging Framework

End-to-End Industry Project

Learn how all Azure services work together by building a complete production-ready Data Engineering project from scratch.

You'll design, develop, orchestrate, monitor, and optimize a complete Azure Data Pipeline similar to those used in enterprise environments.

Real-World Scenarios Covered

This course has been carefully designed around practical business scenarios rather than isolated feature demonstrations.

You'll learn how to solve common challenges faced by Data Engineers, including:

• Dynamic pipeline creation

• Metadata-driven ETL

• Incremental data loading

• API data ingestion

• Multi-table ingestion

• Logging and monitoring

• Error handling

• Production deployment

• Data Warehouse loading

• Performance optimization

• End-to-End ETL orchestration

Certification Preparation

The concepts taught in this course will also help you prepare for Microsoft Azure Data Engineering certifications by building a strong understanding of Azure Data Engineering services and practical implementations.

Who Should Take This Course?

This course is ideal for:

• Aspiring Azure Data Engineers

• Azure Data Factory Developers

• ETL Developers

• SQL Developers

• Data Engineers

• Data Analysts moving into Data Engineering

• Cloud Engineers

• Software Engineers

• Students preparing for Azure interviews

• Professionals preparing for Microsoft Azure Data Engineering certifications