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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

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There are sections in this chapter that details advantages of using Kafka. Also in this chapter, there are sections that details disadvantages and when not to use Kafka. That means, Kafka for us is just a choice suited for the topic that we are covering in this book and also for the SCV use case. The main reason for this choice is because of Kafka's clear advantages; especially when dealing with big data and its associated technologies.

There are other options in market which is a full-fledged messaging system (MOM) and possess rich features compared to Kafka. Some of the alternatives that we think you could look into and replace Kafka are briefly summarized in this section. In no way we mean to say that these cannot be used in our use case, just that we thought Kafka is the best fit. If we are to look at other options in place of Kafka these alternatives are our favorites.

All the technology choices have been made after careful technical analysis and with our book we want to...

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