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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
Author Profile Icon Vivek Mishra
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

Kafka connect


Extensibility is one of the important design principle followed rigorously by Kafka. The Kafka Connect tool makes Kafka extensible. The tool enables Kafka to connect with external systems and helps bring data into it, and also out from it to other systems. It has a common framework, using which custom connectors can be written. More details on Kafka connect can be found in Kafka documentation in https://kafka.apache.org/documentation.html#connect.

The following figure shows how Kafka Connect works.

Figure 13: Kafka connect working

The Kafka connectors are categorized into two:

  • Source Connectors: Connectors which bring data into Kafka topics.
  • Sink Connectors: Connectors which take data away from topics into other external systems

There are a huge list of connectors available, catering to various external systems, using which Kafka can hook onto them. These existing connectors are again categorized broadly into two:

  • Certified connectors: Connectors which are written using the Kafka...
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