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Mastering Hadoop 3

You're reading from  Mastering Hadoop 3

Product type Book
Published in Feb 2019
Publisher Packt
ISBN-13 9781788620444
Pages 544 pages
Edition 1st Edition
Languages
Authors (2):
Chanchal Singh Chanchal Singh
Profile icon Chanchal Singh
Manish Kumar Manish Kumar
Profile icon Manish Kumar
View More author details
Toc

Table of Contents (23) Chapters close

Title Page
Dedication
About Packt
Foreword
Contributors
Preface
1. Journey to Hadoop 3 2. Deep Dive into the Hadoop Distributed File System 3. YARN Resource Management in Hadoop 4. Internals of MapReduce 5. SQL on Hadoop 6. Real-Time Processing Engines 7. Widely Used Hadoop Ecosystem Components 8. Designing Applications in Hadoop 9. Real-Time Stream Processing in Hadoop 10. Machine Learning in Hadoop 11. Hadoop in the Cloud 12. Hadoop Cluster Profiling 13. Who Can Do What in Hadoop 14. Network and Data Security 15. Monitoring Hadoop 1. Other Books You May Enjoy Index

Summary


In this chapter, we covered three popular frameworks: Apache Spark, Apache Flink, and Apache Storm. We briefly covered their architecture, internal working, and the set of APIs that's provided by them. We also covered best practices for all processing engines and studied their importance.

In the next chapter, we will talk about a few widely used components such as Apache Pig, Apache Kafka, Apache Flume, and Apache HBase. We will go though the internals of each component and walk you through certain examples. The primary area of focus would be to understand when to use which component and what the best practices to be followed are with these applications. 

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