Kód: 52265738
Are your data pipelines slowing down, breaking under scale, or becoming too complex to maintain?Modern data systems demand more than scripts that "just work." They require reliability, performance, and the ability to evolve withou ... celý popis
Angličtina
Nákupem získáte 57 bodů
Anotace knihy
Are your data pipelines slowing down, breaking under scale, or becoming too complex to maintain?
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:
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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