Databricks in Action: A Practical Guide to Data Engineering: Build, Optimize, and Deploy Modern Data Pipelines with Apache Spark, PySpark, SQL, and Delta Lake

Databricks in Action: A Practical Guide to Data Engineering

Modern data engineering is no longer just about moving data from one system to another. It is about building reliable, scalable, secure, and production-ready data platforms.

Databricks in Action: A Practical Guide to Data Engineering provides a practical journey through the technologies and concepts that power modern data engineering. From Apache Spark and PySpark to SQL, Delta Lake, streaming, data quality, optimization, governance, and production deployment, this book focuses on the skills needed to build real-world data pipelines.

Inside, readers will explore:

• Data engineering fundamentals and modern lakehouse architecture
• Apache Spark architecture and distributed processing
• PySpark programming and DataFrame operations
• Advanced SQL and data transformation techniques
• Data ingestion and incremental processing
• Bronze, Silver, and Gold data architectures
• Delta Lake, MERGE, time travel, optimization, and maintenance
• Batch and streaming data pipelines
• CDC and slowly changing dimensions
• Data quality, validation, and monitoring
• Spark performance optimization and troubleshooting
• Databricks CLI and REST API automation
• Security, governance, and production deployment
• End-to-end data engineering project practices
• Interview questions and practical preparation

The book also includes detailed appendices covering SQL, PySpark, Spark functions, Delta Lake commands, Databricks CLI and REST API, troubleshooting, interview questions, and a complete project checklist.

Whether you are learning data engineering, preparing for a Databricks-focused role, or looking for a practical reference while building modern data pipelines, this book is designed to help you develop the knowledge and confidence to work with Databricks in real-world environments.

Learn the concepts. Build the pipelines. Troubleshoot the failures. Optimize the system. Think like a production data engineer.