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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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Toc

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

Batch layer for data processing


The core of Hadoop technology has been its ability to perform faster, performant, and optimized batch processes. It proved to be a big success in solving some of the more complex problems of long-running batch processing within organizations. The initial implementations of Hadoop were based on open source Hadoop distributions; however, with the inherent need to make it professionally supported, there were a number of features that were incorporated to make it feasible for enterprise use in terms of provisioning, management, monitoring, and alerting. This resulted in some of the more customized distributions led by MapR, Cloudera, and Hortonworks:

Figure 03: The Hadoop 1 framework

As shown in this image, the Hadoop 1 framework can be broadly classified into Storage and Processing. Storage here is represented by Hadoop Distributed File System (HDFS) while processing is represented as a MapReduce API. Hadoop 2 included many of the improved capabilities with the...

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