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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

Context in Data Lake: data acquisition


One of the V’s of Big Data makes this chapter significant in all aspects in the modern era of any enterprise, namely Velocity. Traditionally, analytics was all done on data collected in the form of data (slow data), but nowadays analytics is done on data flowing in real time and then acted upon in real time to make a meaningful contribution to the business. The business outcome can be in the form of acting on a live Twitter stream of a customer to enhance customer experience or showing up a personalized offer by looking at some of his recent actions on your website. In this chapter, we will be covering mainly the Data Acquisition part of real-time data in our Data Lake.

In Chapter 5, Data Acquisition of Batch Data with Apache Sqoop we have detailed what the Data Acquisition layer is, so I won't be covering that in this section. However there is a significant difference between the data handled in Chapter 5, Data Acquisition of Batch Data with Apache...

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