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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

Why Apache Sqoop


One of the very commonly used tools for data transfer for Apache Hadoop.

In the data acquisition layer, we have chosen Apache Sqoop as the main technology. There are multiple options that can be used in this layer. Also, in place of one technology, there are other options that can be swapped. These options will be discussed in detail to some extent in the last section of this chapter.

Apache Sqoop is one of the main technologies being used to transfer data to and from structured data stores such as RDBMS, traditional data warehouses, and NoSQL data stores to Hadoop. Apache Hadoop finds it very hard to talk to these traditional stores and Sqoop helps to do that integration very easily.

Sqoop helps in the bulk transfer of data from these stores in a very good manner and, because of this reason, Sqoop was chosen as a technology in this layer.

Sqoop also helps to integrate easily with Hadoop based systems such as Apache Oozie, Apache HBase, and Apache Hive.

Apache Oozie is a server...

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