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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

When not to use Flume


In some scenarios, usage of Flume is not the ideal choice. There are other options out there which can be employed to solve those use case and not Flume. Do not choose Flume when:

  • You need more data processing as against transfer of data. They are more suited for other stream processing technologies.
  • You need more batch data transfer scenarios (regular batch as against micro-batch).
  • You need a more available setup with no data loss.
  • You need a durable message with very high scalability requirements (there isn't a scientific quantitative figure for that though).
  • You have a huge number of consumers as this has a very high impact on Flume’s scalability.

          Even through Flume can be dynamically configured in many cases, it does incur downtime in certain configuration changes (topology changes).

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