Modern data systems demand more than scripts that “just work.” They require reliability, performance, and the ability to evolve without constant rewrites. Yet many engineers and analysts struggle with inefficient Spark jobs, unpredictable execution, and rising infrastructure costs.

This book addresses that gap.

Building Scalable Data Systems with Apache Spark 4.x is a practical guide to designing, optimizing, and operating distributed data pipelines using Apache Spark, PySpark, SQL, and lakehouse technologies. It focuses on how Spark actually behaves at scale, so you can build systems that are not only functional, but fast, stable, and production-ready.

You won’t just learn how to write Spark code, you’ll learn how to think like a data systems engineer.

Inside, you will learn how to:

Design end-to-end pipelines from ingestion to output using PySpark and SQL
Understand execution internals like DAGs, jobs, stages, and Catalyst optimization
Optimize performance through partitioning, Adaptive Query Execution (AQE), and efficient joins
Build reliable streaming systems with Structured Streaming and exactly-once semantics
Work with modern storage systems like Delta Lake and Apache Iceberg
Deploy and operate Spark workloads using Kubernetes, monitoring, and resource tuning

Each chapter builds practical intuition, connecting code to execution so you can diagnose bottlenecks, reduce cost, and scale confidently.

If you work as a data engineer, data analyst, backend developer, or data scientist, this book equips you with the skills to move beyond trial-and-error and build systems that perform consistently in real-world environments.

Your data is growing. Your systems should keep up.

Get your copy today and start building data pipelines that scale, perform, and last.

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