Spark in the Wild: Big Data Processing, Streaming Analytics, and Machine Learning Pipelines at Scale

By RICHARD BOOZMAN

Build distributed data systems for real-time analytics, large-scale processing, and production machine learning

Modern data systems operate at enormous scale.

Organizations process terabytes of logs, events, transactions, sensor streams, and machine learning workloads that must remain fast, fault tolerant, and continuously available.

Apache Spark has become one of the most important technologies for handling these large-scale distributed workloads.

"Spark in the Wild" is a practical, engineering-focused guide to building scalable data processing systems, streaming pipelines, and machine learning infrastructure using Spark and modern cloud-native tooling.

This book teaches engineers how to design reliable distributed systems that transform massive volumes of data into actionable intelligence.

Why distributed data engineering matters

Modern organizations face challenges such as:

processing massive datasets efficientlyhandling real-time event streamsscaling machine learning workflowsmanaging distributed compute resourcesmaintaining fault tolerance across clustersoptimizing performance and infrastructure costs

Distributed data systems must balance scalability, reliability, and operational simplicity.

What you will learnfundamentals of distributed data processingSpark architecture and execution modelresilient distributed datasets and DataFrameslarge-scale batch processing workflowsreal-time streaming analytics pipelinesdistributed joins and performance optimizationmachine learning workflows with Sparkcluster resource management and tuningmonitoring and observability for data platformsdeploying Spark workloads in cloud environments
From raw events to intelligent systems

Throughout the book, you will learn how to:

design scalable distributed pipelinesprocess streaming and batch workloads efficientlyoptimize Spark jobs for performance and costbuild fault-tolerant data architecturesmanage production-scale analytics systemsdeploy machine learning pipelines reliably

Each chapter focuses on practical engineering workflows used in real-world data infrastructure teams.

Practical applicationslarge-scale analytics platformsstreaming event processing systemsrecommendation and personalization pipelinesmachine learning feature engineeringcloud-native big data infrastructureenterprise reporting and intelligence systems

These examples reflect real-world distributed data engineering challenges.

Who this book is fordata engineersmachine learning engineersanalytics platform engineersbackend developers working with large-scale datacloud infrastructure teamssoftware engineers building distributed systems

If you want to build scalable, fault-tolerant, and production-ready big data systems using Spark, this book provides the roadmap.

Process at scale.
Stream intelligently.
Engineer distributed data systems that last.

• Format: Paperback
• Language: English
• ISBN: B0H4BBNBTW
• ISBN13: 9798180306296
• Release Date: June 2026
• Publisher: Independently Published
• Length: 296 Pages
• Weight: 0.79 lbs.
• Dimensions: 0.7" x 6.0" x 9.0"