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Data Lake for Enterprises

You're reading from  Data Lake for Enterprises

Product type Book
Published in May 2017
Publisher Packt
ISBN-13 9781787281349
Pages 596 pages
Edition 1st Edition
Languages
Authors (3):
Vivek Mishra Vivek Mishra
Profile icon Vivek Mishra
Tomcy John Tomcy John
Profile icon Tomcy John
Pankaj Misra Pankaj Misra
Profile icon Pankaj Misra
View More author details

Table of Contents (23) Chapters

Title Page
Credits
Foreword
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Part 1 - Overview
Part 2 - Technical Building blocks of Data Lake
Part 3 - Bringing It All Together
1. Introduction to Data 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

Kappa architecture


This book is about building Data Lakes using Lambda Architecture as one of the main layers (Lambda Layer). However, we feel that the readers also need to learn about another minimalist Lambda Architecture under active discussion, namely Kappa architecture. It is more or less similar to lambda, but for the sake of simplicity, the batch layer is removed and only the speed layer is kept. The main idea is to avoid having to compute a batch layer from scratch all the time and try doing almost all of these in real-time or the speed layer. One of the disadvantages of the Lambda Architecture, as detailed above, is to have to keep coding and executing the same logic twice, and this is avoided in the Kappa Architecture.

An image speaks more than a thousand words, and the next diagram compares both the Kappa and Lambda Architectures side by side. In this, you can clearly see that in Kappa, the only missing part is the all-important batch layer:

Figure 11: Kappa (left) and Lambda (right...

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