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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

Why Apache Flink?


The technology choice in this layer was really tough for us. Apache Spark was initially our choice, but Apache Flink had something in it that made us think over and at the time of writing this book, the industry did have some pointers favoring Flink and this made us do the final choice as Flink. However, we could have implemented this layer using Spark and it would have worked well for sure.

This section tries to give the reader reasons for why Flink was chosen. Obviously we have a subsection that gives detail advantages of Flink and those are these primary reasons for the choice.

But before going to the advantages and disadvantages of Flink, lets see how Flink started its journey and what were the advantages it had when it started. Some aspects is definitely its learning from existing similar technologies and that itself is an advantage. Other aspect is new things get developed when there is such a requirement (necessity is the mother of all inventions as stated by the famous...

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