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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

Other options


For the bulk/batch transfer of data from RDBMS to the Hadoop filesystem there aren't many options in the open source world. However, there are possible choices whereby we could transfer data from RDBMS to Hadoop, and this section tries to give you the reader some possible options so that, according to enterprise demands, they can be evaluated and brought into the data lake as technologies if found suitable.

Native big data connectors

Most of the popular databases have connectors, using which data can be extracted and loaded onto the Hadoop filesystem. For example, if your RDBMS is Oracle, Oracle provides a suite of products which integrate the Oracle database with Apache Hadoop. The figure below (Figure 09) shows the full suite of Oracle Big Data connector products and what they do (details taken from www.oracle.com).

Figure 25: Oracle Big Data connector suite of products

Similar to Oracle, MySQL RDBMS has MySQL Applier, which is the native big data connector which can be used...

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