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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

Selection factors for a big data stack for enterprises


For any enterprise to adopt a particular Hadoop Distribution that is commercially supported, there are a few key factors that the enterprise would generally need to evaluate against these distributions in context of its maturity and culture of adoption. Here, we will briefly touch upon some of these key factors.

Technical capabilities

Each of the distributions has its own unique capabilities as well as many other capabilities which are similar to each other. At the minutest details, we can always have a big list of capabilities, but we can focus on some of the prominent ones for the purpose of comparison and evaluation.

Ease of  deployment and maintenance

Many Hadoop distributions, while using common core components, do differentiate themselves from others in terms of ease of deployment and maintenance. This may vary from automation and monitoring interfaces to alerts and upgrades, and so on.

Integration readiness

Many of these distributions...

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