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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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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

Hadoop distributions


One of the first technologies in the open source world to deal with big data for use cases apart from the mining and searching was Hadoop. Hadoop put big data into the hands of enterprises to deal with the so called data existing within the organization.

Being open source, there is a huge community base and backing from big enterprises. The same is the case with Apache Hadoop and earlier, Hadoop distribution was released by Cloudera in 2008. Every distributor adds on many features and also enriches or enhances existing features, making it more attractive for people to adopt and use.

Cloudera is still the most widely used distribution of Hadoop. MapR soon followed by releasing its Hadoop distribution in 2009 and in 2011, Hortonworks released its own distribution. These three players control by far the largest part of the market share at this time.

These distributions not only enhance the existing features; they also try to and integrate many open source products to produce...

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