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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

Workings of Sqoop


For your data lake, you will definitely have to ingest data from traditional applications and data sources. The ingested data, being big, will definitely have to fall into the Hadoop store. Apache Sqoop is one technology that allows you to ingest data from these traditional enterprise data stores into Hadoop with ease.

SQL to Hadoop == SQOOP

The figure below (Figure 03) shows the basic workings of Apache Sqoop. It gives tools to export data from RDBMS to the Hadoop filesystem. It also gives tools to import data from a Hadoop filesystem back to RDBMS.

Figure 03: Basic workings of Sqoop

In our use case, we will be exporting the data stored in RDBMS (PostgreSQL) to the Hadoop File System (HDFS). We will not be looking at Sqoop's import capability in detail, but we will briefly cover that aspect also in this chapter so that you have pretty good knowledge of the different capabilities of this great tool.

As of writing this book, Sqoop has two variations (flavours) called by its major...

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