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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

The Flume Architecture


We discussed in previous section, the architecture principles based on which Flume was conceived, now let's deep dive into the architecture. Let's start off with a very basic diagram detailing the architecture of Flume (Figure 06) and then in the following sections keep diving deep.

Figure 06: Basic Flume Architecture

A simple Flume architecture has three important components, which work together to transfer a data from source to destination in real time fashion (stream or log data). They are:

  • Source: The responsibility of listening to stream data or events and then putting it to the channel
  • Channel: A pipe where events are stored until it has been taken by someone else
  • Sink: The responsibility of taking away events from the channel for further processing (sending to another source) or persisting to a data store. If sink operation fails, it will keep trying until success.

The following table summarizes some of examples for each of the components in the Flume architecture...

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