As organizations scale, the need for efficient and intelligent data platforms continues to grow. Azure Databricks stands out as a leading platform to deliver scalable data processing, collaborative workflows, and seamless cloud integration. The ability to build scalable pipelines using Apache Spark has become a critical skill for modern data professionals. This book provides a practical and structured journey through the complete data engineering lifecycle using Azure Databricks.

It begins with the fundamentals of big data and Spark, then moves into building scalable ETL/ELT pipelines, applying transformations, and enforcing data quality. You will explore architectural patterns in modern data ecosystems and build real-time streaming solutions. Readers will also learn to integrate Databricks with ADF, Power BI, Event Hubs, and other services. The book also covers DevOps and CI/CD using Databricks Asset Bundles.

By the end of this book, you will be equipped with the hands-on skills needed to design, build, validate, and manage production-grade data solutions on Azure Databricks. Whether you are a beginner or an experienced professional, you will gain the confidence to engineer robust data solutions that power analytics, AI, and modern data products.

What you will learn

● Understand big data and data engineering concepts.

● Build scalable ETL and ELT pipelines.

● Build real-time streaming solutions with Structured Streaming.

● Apply data quality checks using Great Expectations.

● Implement CI/CD and DevOps with Databricks Asset Bundles.

● Performance tuning of Databricks workloads.

Who this book is for

This book is designed for data engineers, data architects, data scientists, and IT professionals working with modern data platforms. It is ideal for practitioners looking to build scalable data pipelines and analytical data products, apply robust data quality practices, and master Azure Databricks for real-world data engineering.

Table of Contents

1. Introduction to Big Data and Data Analytics

2. The World of Apache Spark and Databricks

3. Setting up Azure and Databricks Environment

4. Overview of Databricks Free Edition

5. Workspaces, Clusters, and Notebooks

6. Data Ingestion and Storage

7. Data Exploration and Transformation

8. Databricks Delta Tables and Spark SQL

9. Data Validation Techniques

10. Data Visualization in Databricks

11. Real-time Data Processing with Structured Streaming

12. DevOps with Databricks

13. Monitoring Databricks Applications

14. Recommendations for Production Applications