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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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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

Disadvantages of Lambda Architectures


Choosing a Lambda Architecture to develop a Data Lake for your enterprise does incur some inherent disadvantages if some of its aspects are not fully thought through. Some of these are as follows:

  • Due to its different layers, it is generally considered to be complex. Keeping sync between these two layers incurs cost and effort, and this has to be thought through and handled.
  • Because of these two distinct and fully distributed layers (batch and speed), maintenance and support activities are quite hard.
  • There are a good number of technologies that have to be mastered to construct a Lambda-Architecture-driven Data Lake. Getting people who have expertise in these technologies can be troublesome for your recruitment division.
  • Implementing a Lambda Architecture with open sources technologies and then deploying in the cloud can be troublesome. To avoid this, you could very well use cloud technologies to implement Lambda Architecture, but by doing so, the enterprise...
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