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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

Complete working of a Lambda Architecture


The following figure pictorially shows the complete working of Lambda Architecture:

Figure 10: Complete working of Lambda Architecture

As briefly explained earlier, the master data set is maintained and managed in the batch layer. When new data arrives, it is despatched to both, batch and speed layer. Once it reaches the batch layer, at regular batch interval batch views are generated and recomputed from scratch each time. Similarly, the speed layer using the new data generates the speed view whenever the new data arrives in the layer. The serving layer when queried, merges both the speed and batch layer views to generate the appropriate query results.

Once the batch view is generated, the speed view is discarded and till the time new data arrives only bath view needs to be queried as all the data is available in the batch layer itself.

Batch Layer

  • Stored immutable data
  • Constantly growing in size
  • Recomputes views all the time

Speed Layer

  • Constant stream of...
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