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Java Concurrency and Parallelism

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

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781805129264
Length 496 pages
Edition 1st Edition
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Author (1):
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Jay Wang Jay Wang
Author Profile Icon Jay Wang
Jay Wang
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Table of Contents (20) Chapters Close

Preface 1. Part 1: Foundations of Java Concurrency and Parallelism in Cloud Computing FREE CHAPTER
2. Chapter 1: Concurrency, Parallelism, and the Cloud: Navigating the Cloud-Native Landscape 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 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...

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