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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

Other options


As always, it doesn't mean that Apache Flume is the only option that can be used to solve the use case problem in hand. We chose Flume for its merit and advantages especially considering our use case of SCV. There are other options which can be considered and these are discussed in brief in his section.

Apache Flink

Apache Flume is used mainly for data acquisition capability. We will be using Flume to transfer data from source systems sending stream data to the messaging layer (for further processing) and all the way into HDFS.

For transferring data all the way to HDFS, Apache Flume is best fit for stream data. However for getting stream data and then processing is one of the main use case for Apache Flink and it does have additional features suited for this.

This doesn't mean that Apache Flink can be used for transferring data to HDFS, it does have the mechanism but there willn't be so many built-in capabilities. It does have many features as against Flume but they are more on...

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