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Java Concurrency and Parallelism

Java Concurrency and Parallelism

By : Jay Wang
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Java Concurrency and Parallelism

Java Concurrency and Parallelism

5 (1)
By: Jay Wang

Overview of this book

If you’re a software developer, architect, or systems engineer, exploring Java’s concurrency utilities and synchronization in the cloud, this book is an essential resource. Tech visionary Jay Wang, with over three decades of experience transforming industry giants, brings unparalleled expertise to guide you through Java’s concurrency and parallel processing in cloud computing. This comprehensive book starts by establishing the foundational concepts of concurrency and parallelism, vital for cloud-native development, and gives you a complete overview, highlighting challenges and best practices. Wang expertly demonstrates Java’s role in big data, machine learning, microservices, and serverless computing, shedding light on how Java’s tools are effectively utilized in these domains. Complete with practical examples and insights, this book bridges theory with real-world applications, ensuring a holistic understanding of Java in cloud-based scenarios. You’ll navigate advanced topics, such as synchronizing Java’s concurrency with cloud auto-scaling and GPU computing, and be equipped with the skills and foresight to tackle upcoming trends in cloud technology. This book serves as your roadmap to innovation and excellence in Java cloud applications, giving you in-depth knowledge and hands-on practice for mastering Java in the cloud era.
Table of Contents (20 chapters)
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Part 1: Foundations of Java Concurrency and Parallelism in Cloud Computing
7
Part 2: Java's Concurrency in Specialized Domains
12
Part 3: Mastering Concurrency in the Cloud – The Final Frontier

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...

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