Azure Data Engineering

1. Introduction to Data Engineering
  • What is Data Engineering?
  • Data Engineering vs Data Science
  • Importance of Data Engineering in the Cloud Era
  • Data Pipelines, Data Processing vs Storage
2. Introduction to Cloud & Azure
  • What is Cloud Computing?
  • IaaS, PaaS, and SaaS Models
  • Azure Services: Compute, Networking, Storage, etc.
  • Deployment Models: Public, Private, Hybrid
  • Azure Portal, Subscription Management, Resource Grouping
3. Core Data Storage & Management on Azure
  • Azure Storage Fundamentals
  • Structured, Semi-Structured, and Unstructured Data
  • Azure Blob Storage
  • Azure Data Lake Storage Gen2: Features, Tiers, Redundancy
  • Azure SQL Databases
  • Data Lake vs Data Warehouse
  • Choosing Storage Based on Data Type
4. Data Processing in Azure
  • ETL vs ELT: Key Concepts and Differences
  • What is Azure Data Factory (ADF) and Its Importance
  • Data Pipeline Architecture in ADF
  • Dataflow vs Copy Activities
  • Building Simple Pipelines in ADF (Hands-On with Incremental Learning)
5. Transformation & Integration
  • ADF Transformations: Joins, Aggregations, Lookup, Conditionals, Error Handling
  • Introduction to Azure Databricks
  • Spark Basics: SparkSQL, DataFrames, RDDs, Delta Lakes
  • Creating and Managing Notebooks in Databricks
  • Integration of Databricks with Azure
6. Real-Time Data & Big Data Analytics
  • Azure Stream Analytics Overview
  • Streaming Concepts and Real-Time Data Handling
  • Azure Synapse Analytics Overview
  • Integration with Data Lake and SQL Data Warehouse
  • Big Data Analytics Concepts
7. Data Security & Governance
  • Authentication & Authorization: Service Principal, Managed Identity
  • Encryption and Backup Strategies in Azure
  • Data Governance in Azure
  • Introduction to Azure Purview: Data Cataloging & Compliance
8. Data Modeling & Big Data Fundamentals
  • Star and Snowflake Schemas
  • Slowly Changing Dimensions (SCD Types)
  • Introduction to Big Data Technologies: HDFS, ADLS
  • Spark and Hadoop Integration
9. Hands-On Projects
  • Project 1: End-to-End Data Pipeline using ADF
  • Project 2: Real-Time Streaming Pipeline with Azure Stream Analytics & Data Lake
10. Best Practices & Optimization
  • Performance Tuning in ADF and Databricks
  • Optimizing Storage Tiers and Pricing Models
  • Architectural Best Practices for Cloud-based Data Engineering
Core Strengths
  • Cloud-native
  • Azure-focused
  • Data Engineering
Frequently Asked Questions
No, the course is structured to guide beginners through foundational concepts and gradually advance to complex, real-world applications.
You’ll be ready for roles such as Data Engineer, Azure Data Engineer, ETL Developer, Cloud Data Engineer, or Big Data Developer—with the skills to build, manage, and optimize data pipelines in a cloud environment.
The course dives into practical aspects of Azure Databricks, including Spark concepts, DataFrames, Delta Lakes, and Azure integration—enabling you to apply it confidently in real-world projects.