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Data Lake for Enterprises

You're reading from   Data Lake for Enterprises Lambda Architecture for building enterprise data systems

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Product type Paperback
Published in May 2017
Publisher Packt
ISBN-13 9781787281349
Length 596 pages
Edition 1st Edition
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Authors (3):
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Pankaj Misra Pankaj Misra
Author Profile Icon Pankaj Misra
Pankaj Misra
Tomcy John Tomcy John
Author Profile Icon Tomcy John
Tomcy John
Vivek Mishra Vivek Mishra
Author Profile Icon Vivek Mishra
Vivek Mishra
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Toc

Table of Contents (13) Chapters Close

Preface 1. Introduction to Data FREE CHAPTER 2. Comprehensive Concepts of a Data Lake 3. Lambda Architecture as a Pattern for Data Lake 4. Applied Lambda for Data Lake 5. Data Acquisition of Batch Data using Apache Sqoop 6. Data Acquisition of Stream Data using Apache Flume 7. Messaging Layer using Apache Kafka 8. Data Processing using Apache Flink 9. Data Store Using Apache Hadoop 10. Indexed Data Store using Elasticsearch 11. Data Lake Components Working Together 12. Data Lake Use Case Suggestions

Summary

In this chapter, similar to the other chapters in this part of the book, we started with the layer where Apache Hadoop was discussed in detail. We then mapped the technology, namely Hadoop, to this layer. Once we named the technology, we went into detail on the Hadoop technology.

First of all, we gave reasons for choosing this technology and then got into its history, advantages, and disadvantages. Soon, we delved into Hadoop’s working by explaining both Hadoop 1.x and Hadoop 2.x architecture. Since we are using Hadoop 2.x, we explained Hadoop’s architecture components. We then looked at some of the very important components in Hadoop Ecosystem, and we will be using some of these in implementing our SVC use case.

We then delved into some of the other aspects of Hadoop, namely its distributions, HDFS, and its various formats and finally, various deployment modes. We then dived deep into...

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