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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

Summary


In this chapter, as with any other chapter in this part of the book, we started with introducing the layer where the technology would fall. Then we introduced the chosen technology in this layer, namely Apache Flink. We slowly went into the details of Apache Flink. Its architecture was elaborated and many core aspects of this all-important framework were covered in brief. We then got our hands dirty with an actual implementation of Apache Flink technology pertaining to our use case--SCV. We finally explained when to use and when not to use Flink, and closed the chapter with alternatives to Apache Flink.

After reading this chapter, you should have a fair idea of the Data Ingestion layer and the full working and functioning of Apache Flink. Now you also know about Flink’s architecture along with its core components and working. You should have also got hands-on working experience with Flink and a high-level view of the alternatives to Flink.

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