Data Engineering with Azure Databricks
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ISBN: 9789365892857
eISBN: 9789365899559
Authors: Dharmendra Pratap Singh
Rights: Worldwide
Edition: 2026
Pages: 336
Dimension: 7.5*9.25 Inches
Book Type: Paperback
- Description
- Table of Contents
- About the Authors
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.
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
Dharmendra Pratap Singh is a distinguished technology leader in the data and analytics space. He holds a bachelor of technology in computer science and a master of technology in data science. With an illustrious career spanning technology and innovation, Dharmendra has been at the forefront of driving data-driven transformation and advancing technological excellence across multiple organizations.
As a seasoned data and AI leader, Dharmendra brings a profound understanding of designing and implementing next-generation data and AI solutions. His expertise spans the vast data and analytics landscape, encompassing data engineering, machine learning, and GenAI platforms that empower modern, data-driven enterprises to drive business transformation. Dharmendra has won multiple global hackathons across leading organizations, earning recognition for designing and developing advanced, innovative solutions that address real-world challenges.
Dharmendra is also an accomplished technology architect with extensive expertise in the data and AI ecosystem. He is a member of the Global Association of Enterprise Architects (AEA) and holds leading industry credentials, including TOGAF, Azure Solutions Architect Expert, and the Architecture Professional from SEI–CMU, along with several others that reflect his deep proficiency in technology architecture.
In this book, Dharmendra imparts his extensive knowledge and experience to provide readers with a clear, structured path to mastering this transformative platform. His work inspires data professionals and continues to influence the evolving landscape of data analytics.