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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

Why Flume?


This section is dedicated explain you why we have chosen Flume as our technical choice in the technical capability that we look to realize Data Acquisition layer for handling stream/real time data.

With the following subsections, we will first dive into the history and then into Flume’s advantages as well as disadvantages. The advantages detailed are the main reasons for our choice of this technology for dealing with transfer of real-time data into Hadoop.

History of Flume

Apache Flume was developed by Cloudera for handling and moving large amount data produced into Hadoop. Without minimum or no delay (NRT: Near Real Time or Real time) the company wanted the data produced to be moved to Hadoop system, for various analysis to be carried. That was how this beautiful came into existence.

As detailed in previous section, it was initially conceived and developed to take care of a particular use case of collecting and aggregating log data from various source (web servers) into Hadoop for...

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