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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

Flume architecture principles


Any technology piece to be successful, should have clearly defined architecture principles based on which its design is created and then evolved throughout. Flume also comes in one such software and up next are some of the architecture principles based on which Flume was designed (some of those got introduced as part of Flume NG):

  • Reliability: The capability of continuously accepting stream data and events without losing any data, in a variety of failure scenarios (mostly partial failures). One of the core architecture principles taken very seriously by Flume is fault-tolerance, which means that even if some components fail or misbehave, some hardware issues pop up, or if bandwidth or network behaves bad, Flume will accept these as facts of life in most cases and carry on doing its main job without shutting down completely. Flume does guarantee that the data reaching the Flume Agent will eventually be handed over to other components as long as the agent is kept...
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