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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
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

Knowing Hadoop distributions


A Big Data ecosystem consists of multiple capabilities, and for every capability in the ecosystem, there are one or more frameworks. Different distributions realize these capabilities in their own specific ways and also have some additional edge over other competitors in the same space.

Figure 01: Hadoop distributions

Shown here are some of the leading distributions of Hadoop framework, wherein Cloudera, Hortonworks, and MapR are the leaders in commercial space while Apache Hadoop is an open source distribution. These commercial offerings, while having their own specific capabilities, are largely based on the specifications of the open source Hadoop framework.

Just to put a few things into the perspective of why a Hadoop distribution should be chosen, unfortunately there is no straight answer for it. However, we can compare these distributions across various dimensions that we may be interested in for evaluation.

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