Basics to Advanced: Azure Synapse Analytics Hands-On Project

Build complete project only with Azure Synapse Analytics focused on PySpark includes delta lake and spark Optimizations

In this comprehensive, Basics to Advanced: Azure Synapse Analytics Hands-on project course, you are going to gain EVERY core concept, processing technique, and practical skill required to work confidently with Spark, SQL Pools, Delta Lake, and Power BI in real enterprise environments.

This is not just a tool walkthrough.

This course explains how data processing evolved, why Spark replaced traditional systems, and how Azure Synapse Analytics brings SQL and Spark together to solve real data engineering problems.

Inside this end-to-end Azure Synapse Analytics program, you will master:

1. AZURE SYNAPSE FOUNDATIONS & EVOLUTION

Understand the origin of Azure Synapse Analytics, its purpose, and how modern analytics platforms evolved (Introduction + Origin of Synapse)

2. ENVIRONMENT & WORKSPACE SETUP

Set up Synapse environments, Spark pools, SQL pools, and access configurations correctly (Environment Setup)

3. SERVERLESS SQL POOL MASTERCLASS

Query data directly from data lakes using Serverless SQL Pool with real analytics scenarios (Serverless SQL Pool)

4. DATA PROCESSING BEFORE SPARK

Understand traditional data processing limitations and why distributed systems became necessary (History before Spark)

5. EMERGENCE OF SPARK

Learn why Spark was created and how it transformed large-scale data processing (Emergence of Spark)

6. SPARK CORE CONCEPTS IN DEPTH

Build strong foundations in RDDs, DataFrames, execution model, and distributed processing (Spark Core Concepts)

7. PYSPARK DATA TRANSFORMATIONS – BASICS

Perform filtering, selection, null handling, duplicates removal, and aggregations using PySpark (Transformations 1 & 2)

8. PYSPARK DATA MANIPULATION

Apply real-world transformations including data reshaping, manipulation, and enrichment (Transformation 3)

9. SYNAPSE SPARK & MSSPARKUTILS

Work with Synapse-specific Spark utilities and Spark SQL for enterprise data engineering (PySpark 4 & 5)

10. ADVANCED PYSPARK TRANSFORMATIONS

Implement joins, string manipulation, sorting, window functions, pivoting, and conversions (Transformations 6–9)

11. SCHEMA MANAGEMENT & UDFS

Handle schema definitions, evolution, and custom logic using PySpark UDFs (Transformations 10 & 11)

12. DEDICATED SQL POOL FUNDAMENTALS

Understand Dedicated SQL Pool architecture, performance concepts, and analytics workloads (Dedicated SQL Pool)

13. REPORTING WITH POWER BI

Connect Synapse data to Power BI and build reporting-ready datasets (Reporting to Power BI)

14. SPARK PERFORMANCE OPTIMIZATION

Apply Spark optimization techniques to improve execution time and resource efficiency (Spark Optimisation)

15. DELTA LAKE WITH SYNAPSE

Implement Delta Lake for ACID transactions, schema evolution, time travel, and reliable pipelines (Delta Lake)