Apache Spark for Distributed Systems Engineers
Language: English
Published by Devlin Nexley, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 25.04
Quantity: 2 available
Add to basketItem description from seller
nach der Bestellung gedruckt Neuware - Printed after ordering - 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 modelDAG construction, stage decomposition, and task scheduling mechanicsShuffle architecture, data movement patterns, and network bottlenecksMemory management, execution optimization, and JVM runtime behaviorFault tolerance through lineage reconstruction and retry semanticsQuery execution via Spark SQL, Catalyst optimizer, and Tungsten engineStructured Streaming and incremental computation modelsPerformance bottlenecks, skew handling, and production tuning strategiesCloud-native execution on object storage systems and KubernetesIntegration with modern lakehouse ecosystems such as Delta Lake, Iceberg, and HudiRather 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.…
Seller Inventory # 9789326137003
- Title
- Apache Spark for Distributed Systems Engineers
- Author
- Devlin Nexley
- Publisher
- Devlin Nexley
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 9326137007
- ISBN 13
- 9789326137003
- Item weight
- 276 grams
- Dimensions
- 229x152x10 mm
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.
"Synopsis" may belong to another edition of this title.
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