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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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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 in Data Lake - messaging layer


In this chapter, we are dealing with a technology which constitutes one of the core layers of Data Lake namely, the messaging layer. Its crucial to have a fully functional messaging layer for dealing with a real-time data stream flowing in from different applications in an enterprise.

The technology that we have shortlisted to do this very important job of handling such, stream data is Apache Kafka. This chapter will take you through the functioning of messaging layer and then deep dive into the technology, Kafka.

Messaging layer

In Chapter 2, Comprehensive Concepts of a Data Lake, you already a high-level view of the messaging layer and how it works, especially in the context of Data Lake.

Figure 01: Data Lake: Messaging layer

The messaging layer in Data Lake takes care of as mentioned in the bulleted list has a set of functions/capabilities:

  • One of the core capabilities of this layer is it's ability to decouple both the source (producer) and destination...
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