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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
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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

When not to use Sqoop


Sqoop is the best suited tool when your data lives in database systems such as Oracle, MySQL, PostgreSQL, and Teradata; Sqoop is not a best fit for event driven data handling. For event driven data, it's apt to go for Apache Flume (Chapter 7, Messaging Layer with Apache Kafka in this book covers Flume in detail) as against Sqoop. To summarize, below are the points when Sqoop should not be used:

  • For event driven data.
  • For handling and transferring data which are streamed from various business applications. For example data streamed using JMS from a source system.
  • For handling real-time data as opposed to regular bulk/batch data and micro-batch.
  • Handling data which is in the form of log files generated in different web servers where the business application is hosted.
  • If the source data store should not be put under pressure when a Sqoop job is being executed, it's better to avoid Sqoop. Also, if the bulk/batch have high volumes of data, the pressure that it would put on...
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