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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

Advantages of Lambda Architecture


There are various advantages because of which we chose Lambda Architecture for construction Data Lake for the enterprise. Some of these advantages can be given as:

  • Data stored is in raw format. Because of this, at any time, new algorithms, analytics, or new business use cases can be applied to the Data Lake by simply creating new batch and speed views. This is one of the biggest advantages of traditional data warehouses in which data is cleansed and stored. Because of this, new use cases would need to change the data schema, and this is usually time and effort consuming.
  • One of its very own important principles, namely recomputation, helps correct fault tolerance without much trouble. As more and more data comes into the lake, data loss and corruption can be something that cannot be afforded. Because of this recomputation, at any moment we can recompute, roll back, or flush data to correct these errors.
  • Lambda Architecture separates different responsibilities...
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