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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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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

Flume working example


In this section, as always throughout this part of the book, we will cover a full working example for the technology; towards the end of this section, there will be a dedicated section that covers how in our use case SCV is implemented, showing real code snippets.

Installation and Configuration

This step details most of the installation stuff that has to be done to make Flume working. This is a pre-requisite to be dealt with.

Step 1: Installing and verifying Flume

In this section we will install Apache Flume and then verify its installation. Follow the given steps for complete installation:

  1. Download the Apache Flume binary distribution with the following command; we will be using the current version of Apache Flume, which is 1.7.0.
wget http://www-us.apache.org/dist/flume/1.7.0/apache-flume-1.7.0-bin.tar.gz
  1. Once downloaded, change the directory to a location where you will want to extract contents by using the following command:
tar -zxvf ${DOWNLOAD_DIRECTORY}/apache-flume...
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