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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

Other flume components


In addition to main components in Flume, there are other very important components. These components will be discussed in some detail in this section. The following figure shows all of these components working together:

Figure 19: Other Flume components working together

The following subsection gets deep into working and responsibility of each of the components in the preceding figure (Figure 19). Let's get started and understand how these components will help you in designing the right Flume component arrangement to execute your use case successfully.

Channel processor

As shown in Figure 19 the source sends the events to the channel processor. Every source has its own channel processor and for persisting the event in the channel, the source delegates the work to the channel processor, which actually does the job of persisting according to the channel type.

Interceptor

As seen in the preceding figure, the channel processor then passes the events to the interceptor. Channel...

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