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Big Data Visualization
Big Data Visualization

Big Data Visualization: Bring scalability and dynamics to your Big Data visualization

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Big Data Visualization

Chapter 2.  Access, Speed, and Storage with Hadoop

This chapter aims to target the challenge of storing and accessing large volumes and varieties (structured or unstructured) of data offering working examples demonstrating solutions for effectively addressing these issues.

Since it is expected that you are somewhat familiar with Hadoop, this chapter starts with a brief overview of the technology, but doesn't intend to cover all of the details as the goal is to provide a demonstration using Hadoop as a technology to address the challenge of storing and accessing big data.

In addition, in an effort towards completeness, we'll touch on the possible alternatives to using Hadoop, such as Apache Spark and even a simple scripting solution.

By the end of this chapter, the reader should have an idea of what Hadoop is and how it works, should have acquired an appreciation for the reasoning for leveraging Hadoop to store, and should have accessed big data and also have worked through...

About Hadoop

Let's start out with an explanation of Hadoop that is generally circulated.

As per Apache Hadoop wikipedia.org, 2016:

"Hadoop is an open-source software "framework" for distributed storage and distributed processing (of very large datasets) on computer clusters built from commodity hardware."

The following is a visualization that may help understand the master-to-slave architecture used by Hadoop:

About Hadoop

Hadoop uses an architecture called MapReduce. This is a design that designates a processor (in a cluster of processors) as the master, which controls distributing or mapping tasks to other slave processors to process your data, thus reducing the processing performed by the cluster of processors to a single output result. So, you can now see that the name mapped reduction or MapReduce (of processing tasks) makes sense.

Hadoop is able to take your data and split it up (or distribute it) over a number of computers that have space or resources available.

These...

Log files and Excel

Let's consider a somewhat realistic use case where you have been provided a number of modified web log files that you want to create some visualizations from.

In Chapter 4, Addressing Big Data Quality, we will discuss data profiling (in regards to data quality), but for now, we'll assume that we know the following about our data files:

  • The files are of various sizes and somewhat unstructured.
  • The data in the files contain information logged by Internet users.
  • The data includes such things as computer IP addresses, a date, timestamp, and a web address/URL. There is more information in the files, but for our exercise here we really just want to create a graphical representation showing the number of times each web address was hit during each month (there are actually software packages that provide web statistics, but we'll suppose you don't have access to any of them).

The following is a sample transaction (record) from one of our files:

221.738.236 - - ...

Hadoop and big data

In this section, we'll consider why Hadoop is actually a very good choice for storing and accessing big data.

Imagine you want to process data, a lot of data. In our previous example, we considered the scenario where machine generated web logging files are being produced and we want to leverage information within those files to perform some analytics and produce some (hopefully) compelling data visualizations.

Using R worked here, but if we extend the scenario with the idea that we will continue to receive web log files over time and the size of those files will increase, R might not be a feasible answer.

Entering Hadoop

Hadoop (as the product documentation says) is not your average database. In fact, Hadoop can store all kinds of data from many servers and websites and corporate vaults--as much as you might need or want to gather. In addition, Hadoop spreads your work across hundreds or thousands of processors and storage drives working in parallel all at the same...

Example 1

In our earlier scenario, we have multiple machine generated web log files. Although as we have seen that the web log files are too large to deal with MS Excel, they individually do not meet the criteria of big data. However, continuing the scenario, let's suppose we now have more than the original files as our website is perhaps generating multiple files each day. Given this presumption, we need a secure repository in which to store and then (hopefully) easily access our files.

Defining the environment

As I've mentioned, AWS provides us the ability to leverage Hadoop technology without spending all the time required to create and manage a new environment.

To use this environment, you need to first have an AWS account. Since this chapter is focused on loading and accessing big data files in a Hadoop enabled environment, we'll skip over how to create an account (to create an account, the reader can use a web browser to open: http://aws.amazon.com, and then click on Create...

About Hadoop


Let's start out with an explanation of Hadoop that is generally circulated.

As per Apache Hadoop wikipedia.org, 2016:

"Hadoop is an open-source software "framework" for distributed storage and distributed processing (of very large datasets) on computer clusters built from commodity hardware."

The following is a visualization that may help understand the master-to-slave architecture used by Hadoop:

Hadoop uses an architecture called MapReduce. This is a design that designates a processor (in a cluster of processors) as the master, which controls distributing or mapping tasks to other slave processors to process your data, thus reducing the processing performed by the cluster of processors to a single output result. So, you can now see that the name mapped reduction or MapReduce (of processing tasks) makes sense.

Hadoop is able to take your data and split it up (or distribute it) over a number of computers that have space or resources available.

These computers need not be high...

Log files and Excel


Let's consider a somewhat realistic use case where you have been provided a number of modified web log files that you want to create some visualizations from.

In Chapter 4, Addressing Big Data Quality, we will discuss data profiling (in regards to data quality), but for now, we'll assume that we know the following about our data files:

  • The files are of various sizes and somewhat unstructured.

  • The data in the files contain information logged by Internet users.

  • The data includes such things as computer IP addresses, a date, timestamp, and a web address/URL. There is more information in the files, but for our exercise here we really just want to create a graphical representation showing the number of times each web address was hit during each month (there are actually software packages that provide web statistics, but we'll suppose you don't have access to any of them).

The following is a sample transaction (record) from one of our files:

221.738.236 - - [15/Oct/2014:6:55:2] GET...
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Key benefits

  • • This unique guide teaches you how to visualize your cluttered, huge amounts of big data with ease
  • • It is rich with ample options and solid use cases for big data visualization, and is a must-have book for your shelf
  • • Improve your decision-making by visualizing your big data the right way

Description

Gain valuable insight into big data analytics with this book. Covering the tools you need to analyse data, together with IBM certified expert James Miller?s insight, this book is the key to data visualization success. ? Learn the tools & techniques to process big data for efficient data visualization ? Packed with insightful real-world use cases ? Addresses the difficulties faced by professionals in the field of big data analytics

Who is this book for?

Who is this book for? ? Data analysis beginners & data analysts who want to use visualization for more powerful analysis ? Knowledge of big data platform tools such as Hadoop & programming languages such as R is required

What you will learn

  • Get to grips with the basics of big data visualization before moving onto data storage, adding context to data using R, and addressing data quality issues. Learn how to use D3 and dashboards to display and present results, and how to use Python to deal with anomalies.
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Publication date : Feb 28, 2017
Length: 304 pages
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Table of Contents

8 Chapters
1. Introduction to Big Data Visualization Chevron down icon Chevron up icon
2. Access, Speed, and Storage with Hadoop Chevron down icon Chevron up icon
3. Understanding Your Data Using R Chevron down icon Chevron up icon
4. Addressing Big Data Quality Chevron down icon Chevron up icon
5. Displaying Results Using D3 Chevron down icon Chevron up icon
6. Dashboards for Big Data - Tableau Chevron down icon Chevron up icon
7. Dealing with Outliers Using Python Chevron down icon Chevron up icon
8. Big Data Operational Intelligence with Splunk Chevron down icon Chevron up icon

Customer reviews

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JM Dec 05, 2018
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Really nice, good price
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