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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

Applied lambda


Enterprise-level Data Lake is one of the applications of the Lambda Architecture pattern. In this book, we are going to cover this in more detail. However, there are other use cases where this pattern can be applied and this section tries to cover these.

Enterprise-level log analysis

One of the very common use cases for this pattern is log ingestion and various analytics that surround it. The ELK (Elasticsearch, Logstash, Kibana) stack is a leading one in this space, but this pattern could very well be used. The logs can vary from conventional application logs to different types of logs produced by various software and hardware components. If we need to have an enterprise level log management and analytical capability this pattern is indeed a good choice. These logs are produced in large quantities and at very high velocity. Also these are immutable in nature and does need to have an order in place for analyst (may be a developer of an application or a security data scientists...

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