9781788620444-1788620445-Mastering Hadoop 3: Big data processing at scale to unlock unique business insights

Mastering Hadoop 3: Big data processing at scale to unlock unique business insights

ISBN-13: 9781788620444
ISBN-10: 1788620445
Author: Singh, Chanchal, Kumar, Manish
Publication date: 2019
Publisher: Packt Publishing
Format: Paperback 544 pages
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Book details

ISBN-13: 9781788620444
ISBN-10: 1788620445
Author: Singh, Chanchal, Kumar, Manish
Publication date: 2019
Publisher: Packt Publishing
Format: Paperback 544 pages

Summary

Acknowledged authors Singh, Chanchal, Kumar, Manish wrote Mastering Hadoop 3: Big data processing at scale to unlock unique business insights comprising 544 pages back in 2019. Textbook and eTextbook are published under ISBN 1788620445 and 9781788620444. Since then Mastering Hadoop 3: Big data processing at scale to unlock unique business insights textbook was available to sell back to BooksRun online for the top buyback price of $ 2.00 or rent at the marketplace.

Description

A comprehensive guide to mastering the most advanced Hadoop 3 concepts

Key Features
  • Get to grips with the newly introduced features and capabilities of Hadoop 3
  • Crunch and process data using MapReduce, YARN, and a host of tools within the Hadoop ecosystem
  • Sharpen your Hadoop skills with real-world case studies and code
Book Description

Apache Hadoop is one of the most popular big data solutions for distributed storage and for processing large chunks of data. With Hadoop 3, Apache promises to provide a high-performance, more fault-tolerant, and highly efficient big data processing platform, with a focus on improved scalability and increased efficiency.

With this guide, you'll understand advanced concepts of the Hadoop ecosystem tool. You'll learn how Hadoop works internally, study advanced concepts of different ecosystem tools, discover solutions to real-world use cases, and understand how to secure your cluster. It will then walk you through HDFS, YARN, MapReduce, and Hadoop 3 concepts. You'll be able to address common challenges like using Kafka efficiently, designing low latency, reliable message delivery Kafka systems, and handling high data volumes. As you advance, you'll discover how to address major challenges when building an enterprise-grade messaging system, and how to use different stream processing systems along with Kafka to fulfil your enterprise goals.

By the end of this book, you'll have a complete understanding of how components in the Hadoop ecosystem are effectively integrated to implement a fast and reliable data pipeline, and you'll be equipped to tackle a range of real-world problems in data pipelines.

What you will learn
  • Gain an in-depth understanding of distributed computing using Hadoop 3
  • Develop enterprise-grade applications using Apache Spark, Flink, and more
  • Build scalable and high-performance Hadoop data pipelines with security, monitoring, and data governance
  • Explore batch data processing patterns and how to model data in Hadoop
  • Master best practices for enterprises using, or planning to use, Hadoop 3 as a data platform
  • Understand security aspects of Hadoop, including authorization and authentication
Who this book is for

If you want to become a big data professional by mastering the advanced concepts of Hadoop, this book is for you. You'll also find this book useful if you're a Hadoop professional looking to strengthen your knowledge of the Hadoop ecosystem. Fundamental knowledge of the Java programming language and basics of Hadoop is necessary to get started with this book.

Table of Contents
  1. Journey to Hadoop 3
  2. Deep Dive into Hadoop Distributed File System
  3. YARN Resource Management in Hadoop
  4. Internals of Map Reduce
  5. SQL on Hadoop
  6. Real Time Processing Engines
  7. Widely used Hadoop Ecosystem Component
  8. Designing Applications in Hadoop
  9. Real Time/Micro Batch Processing in Hadoop
  10. Machine Learning in Hadoop
  11. Hadoop in Cloud
  12. Hadoop Cluster Profiling
  13. Who can do What in Hadoop
  14. Network and Data Security
  15. Monitoring Hadoop
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