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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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Toc

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

Context Routing


As explained earlier, event has two main parts namely Header and Payload. Header (Key/Value pair) values can be used and accordingly routing defined. Two components where the routing selection can be decided are:

  • Channel: A channel can be selected according to the header values. Custom component namely Channel Selector can be written which can have code written to select the channel desired for achieving your use case.
  • Sink: As before, header values can be used to make decisions to select the right sink. Also, within the sink different operations can be performed by writing custom sink which can do whatever your use case require. There are some default header values which can also be used to do sophisticated stuff for your use case selected.

Basically you can introduce any number of headers and using which your components can do the right stuff. Doing this, the flume components behaves dynamically in all aspects.

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