You're reading from Java Concurrency and Parallelism Master advanced Java techniques for cloud-based applications through concurrency and parallelism

Product type Paperback

Published in Aug 2024

Publisher Packt

ISBN-13 9781805129264

Length 496 pages

Edition 1st Edition

Languages

Java

Tools

Boost

Concepts

Application Development

Author (1):

Jay Wang

View More author details

Table of Contents (20) Chapters

Preface

1. Part 1: Foundations of Java Concurrency and Parallelism in Cloud Computing

2. Chapter 1: Concurrency, Parallelism, and the Cloud: Navigating the Cloud-Native Landscape FREE CHAPTER

3. Chapter 2: Introduction to Java’s Concurrency Foundations: Threads, Processes, and Beyond

4. Chapter 3: Mastering Parallelism in Java

5. Chapter 4: Java Concurrency Utilities and Testing in the Cloud Era

6. Chapter 5: Mastering Concurrency Patterns in Cloud Computing

7. Part 2: Java's Concurrency in Specialized Domains

8. Chapter 6: Java and Big Data – a Collaborative Odyssey

9. Chapter 7: Concurrency in Java for Machine Learning

10. Chapter 8: Microservices in the Cloud and Java’s Concurrency

11. Chapter 9: Serverless Computing and Java’s Concurrent Capabilities

12. Part 3: Mastering Concurrency in the Cloud – The Final Frontier

13. Chapter 10: Synchronizing Java’s Concurrency with Cloud Auto-Scaling Dynamics

14. Chapter 11: Advanced Java Concurrency Practices in Cloud Computing

15. Chapter 12: The Horizon Ahead

16. Index

Why subscribe?

17. Other Books You May Enjoy

Appendix A: Setting up a Cloud-Native Java Environment

1. Appendix B: Resources and Further Reading

Java and Big Data – a Collaborative Odyssey

Embark on a transformative journey as we harness the power of Java to navigate the vast landscape of big data. In this chapter, we’ll explore how Java’s proficiency in distributed computing, coupled with its robust ecosystem of tools and frameworks, empowers you to tackle the complexities of processing, storing, and extracting insights from massive datasets. As we delve into the world of big data, we’ll showcase how Apache Hadoop and Apache Spark seamlessly integrate with Java to overcome the limitations of conventional methods.

Throughout this chapter, you’ll gain hands-on experience in building scalable data processing pipelines, using Java alongside the Hadoop and Spark frameworks. We’ll explore Hadoop’s core components, such as Hadoop Distributed File System (HDFS) and MapReduce, and dive deep into Apache Spark, focusing on its primary abstractions, including Resilient Distributed Datasets...