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

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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

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Appendix A: Setting up a Cloud-Native Java Environment

1. Appendix B: Resources and Further Reading

Hadoop and Spark equivalents in major cloud platforms

While Apache Hadoop and Apache Spark are widely used in on-premises big data processing, major cloud platforms offer managed services that provide similar capabilities without the need to set up and maintain the underlying infrastructure. In this section, we’ll explore the equivalent services to Hadoop and Spark in AWS, Azure, and GCP:

Amazon Web Services (AWS):
- Amazon Elastic MapReduce: Amazon Elastic MapReduce (EMR) is a managed cluster platform that simplifies running big data frameworks, including Apache Hadoop and Apache Spark. It provides a scalable and cost-effective way to process and analyze large volumes of data. EMR supports various Hadoop ecosystem tools such as Hive, Pig, and HBase. It also integrates with other AWS services such as Amazon S3 for data storage and Amazon Kinesis for real-time data streaming.
- Amazon Simple Storage Service: Amazon Simple Storage Service (S3) is an object storage service that...