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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 to use Sqoop


Apache Sqoop could be employed for many of the data transfer requirements in a data lake, which has HDFS as the main data storage for incoming data from various systems. These bullet points give some of the cases where Apache Sqoop makes more sense:

  • For regular batch and micro-batch to transfer data to and from RDBMS to Hadoop (HDFS/Hive/HBase), use Apache Sqoop. Apache Sqoop is one of the main and widely used technologies in the data acquisition layer.
  • For transferring data from NoSQL data stores like MongoDB and Cassandra into the Hadoop filesystem.
  • Enterprises having good amounts of applications whose stores are based on RDBMS, Sqoop is the best option to transfer data into a Data Lake.
  • Hadoop is a de-facto standard for storing massive data. Sqoop allows you to transfer data easily into HDFS from a traditional database with ease.
  • Use Sqoop when performance is required, as it is able to split and parallelize data transfer.
  • Sqoop has a concept of connectors and, if your enterprise...
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