Apache Spark for Distributed Systems Engineers

Devlin Nexley

ISBN 10: 9326137007 / ISBN 13: 9789326137003

Published by Devlin Nexley, 2026

About this Item

Apache Spark for Distributed Systems Engineers | Devlin Nexley | Taschenbuch | Englisch | 2026 | Devlin Nexley | EAN 9789326137003 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.

Seller Inventory # 136944523

Bibliographic Details

• Title: Apache Spark for Distributed Systems Engineers
• Publisher: Devlin Nexley
• Publication Date: 2026
• Language: English
• ISBN 10: 9326137007
• ISBN 13: 9789326137003
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 229W x 152H x 10D millimeters 229 millimeters width by 152 millimeters height by 10 millimeters depth
• Weight: 276 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Apache Spark is not just a data processing framework-it is a distributed execution system built on deep principles of cluster computing, DAG-based scheduling, memory-aware computation, and fault-tolerant design.

This book provides a rigorous, systems-level examination of Spark as a production-grade distributed execution engine. It moves beyond APIs, tutorials, and surface-level usage patterns to expose the internal mechanisms that govern how Spark actually executes workloads at scale.

Designed for experienced engineers working in distributed systems, backend infrastructure, and data platform engineering, this book dissects Spark as an architectural system rather than a development tool.

Inside, you will explore how Spark transforms high-level computations into distributed execution graphs, how it schedules and coordinates work across clusters, and how it manages the complexity of large-scale data movement in cloud-native environments.

Key areas covered include:

Internal architecture of Spark's driver, executors, and cluster coordination model

DAG construction, stage decomposition, and task scheduling mechanics

Shuffle architecture, data movement patterns, and network bottlenecks

Memory management, execution optimization, and JVM runtime behavior

Fault tolerance through lineage reconstruction and retry semantics

Query execution via Spark SQL, Catalyst optimizer, and Tungsten engine

Structured Streaming and incremental computation models

Performance bottlenecks, skew handling, and production tuning strategies

Cloud-native execution on object storage systems and Kubernetes

Integration with modern lakehouse ecosystems such as Delta Lake, Iceberg, and Hudi

Rather than presenting Spark as a tool to be used, this book treats it as a distributed systems case study-revealing how large-scale data infrastructure is engineered, optimized, and operated under real production constraints.

By the end, readers will understand not only how Spark works, but why its architecture is designed the way it is, what trade-offs shape its execution model, and how it fits into the broader evolution of modern distributed data platforms.

This is a book for engineers who build systems, not just pipelines.