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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

Other Hadoop Processing Options

Apache Hadoop is something that will always pop up whenever a big data term is used. It has almost become a mandatory piece when dealing with Big Data. There is no doubt that Hadoop is an excellent choice, but it does have some inherent aspects that put a doubt in developers' minds when the choice has to be made, especially when big data and its processing is ever increasing in any enterprise, obviously due to changing business dynamics. Some of its pointed disadvantages are Hadoop's complexity and the way it actually does execution. Due to these reasons, there have been some recent innovations to simplify Hadoop processing further, and some of these simplifications have been brought in by the advent of Pig scripts and Apache Spark.

Pig scripts provide a good alternate to simplify MapReduce activity with pig Latin language, while still enabling non-Java developers...

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