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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
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Tomcy John
Vivek Mishra Vivek Mishra
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Vivek Mishra
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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

Working of Hadoop

Let's now see the internals of Hadoop and its components, it's architecture, and how it works in this section. We will start off by understanding some of Hadoop’s core architecture principles, and then we will explain its architecture and important components in detail.

Hadoop core architecture principles

Hadoop was built and conceived with well-defined architecture goals and principles, as listed here, (the following are in no way authoritative as we can't find one; rather we gathered this from https://goo.gl/3nvERl):

  • Linear scalability (Scale-Out rather than Scale-Up): Add more nodes for scalability to increase data storage and computing power.
  • Bring code to data rather than data to code: In big data, data is usually...
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