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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

Data Acquisition of Batch Data using Apache Sqoop

Now that we have discussed some of the essential elements of a data lake in the context of Lambda Architecture, it is imperative that the complete story around data lake starts from capturing the data from source systems, which we are referring to as Data Acquisition.

Data can be acquired from various systems, in which data may exist in various forms. Each of these data formats would need a specific way of data handling such that the data can be acquired from the source system and put to action within the boundaries of data lake.

In this chapter, we would be specifically looking at acquiring data from relational data sources, such as a Relational DataBase Management System (RDBMS) and discuss specific patterns for the same. When it comes to capturing data specifically from relational data sources, Apache Sqoop is one of the primary frameworks that has been...

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