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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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Toc

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

Why Apache Kafka


We are using Apache Kafka as the stream data platform (MOM: message-oriented middleware). Core reasons for choosing Kafka is it's high reliability and ability to deal with data with a very low latency.

Note

Message-oriented middleware (MOM) is software or hardware infrastructure supporting the sending and receiving of messages between distributed systems.

- Wikipedia

Apache Kafka has some key attributes attached to it making it an ideal choice for us in achieving the capability that we are looking to implement the Data Lake. They are bulleted below:

  • Scalability: Capable of handling high-velocity and high-volume data. Hundreds of megabytes per second throughput with terabytes of data.
  • Distributed: Kafka is distributed by design and handles some of the distributed capabilities as follows:
    • Replication: The replication feature is one of the default features which needs to be available for any distributed enabled technology and Kafka has this feature built-in.
    • Partition capable: Again...
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