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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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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

Working of Flink


An image conveys much more than a paragraph and because of that reason we will start this section with a figure. The functioning of Flink is as shown in the following figure:

Figure 05: Functioning of Flink

Flink is capable of taking in both batch and stream data. It operates on batch data as if it is another form of stream data and this itself is quite a unique feature of Flink. We have in one of the chapters in Part 1 explained a bit on Kappa Architecture was explained, in which all data is being considered and dealt with stream data and Flink uses that exact principle in its architecture and implementation.

In the preceding figure, both types of data (batch and stream) from various source systems gets into Flink. The Flink program submits the job and using master and worker, deals with these data and produces output.

Flink architecture

The crux of the Flink architecture as shown in the preceding figure are three important components working together namely:

  • Client
  • Job Manager...
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