Book Image

Practical Big Data Analytics

By : Nataraj Dasgupta
Book Image

Practical Big Data Analytics

By: Nataraj Dasgupta

Overview of this book

Big Data analytics relates to the strategies used by organizations to collect, organize, and analyze large amounts of data to uncover valuable business insights that cannot be analyzed through traditional systems. Crafting an enterprise-scale cost-efficient Big Data and machine learning solution to uncover insights and value from your organization’s data is a challenge. Today, with hundreds of new Big Data systems, machine learning packages, and BI tools, selecting the right combination of technologies is an even greater challenge. This book will help you do that. With the help of this guide, you will be able to bridge the gap between the theoretical world of technology and the practical reality of building corporate Big Data and data science platforms. You will get hands-on exposure to Hadoop and Spark, build machine learning dashboards using R and R Shiny, create web-based apps using NoSQL databases such as MongoDB, and even learn how to write R code for neural networks. By the end of the book, you will have a very clear and concrete understanding of what Big Data analytics means, how it drives revenues for organizations, and how you can develop your own Big Data analytics solution using the different tools and methods articulated in this book.
Table of Contents (16 chapters)
Title Page
Packt Upsell
Contributors
Preface

Summary


This chapter provided a technical overview of Hadoop. We discussed the core components and core concepts that are fundamental to Hadoop, such as MapReduce and HDFS. We also looked at the technical challenges and considerations of using Hadoop. While it may appear simple in concept, the inner workings and a formal administration of a Hadoop architecture can be fairly complex. In this chapter we highlighted a few of them.

We concluded with a hands-on exercise on Hadoop using the Cloudera Distribution. For this tutorial, we used the CDH Virtual Machine downloaded earlier from Cloudera's website.

In the next chapter, we will look at NoSQL, an alternative or a complementary solution to Hadoop depending upon your individual and/or organization al needs. While Hadoop offers a far richer set of capabilities, if your intended use case(s) can be done with simply NoSQL solutions, the latter may be an easier choice in terms of the effort required.