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Practical Big Data Analytics
Practical Big Data Analytics

Practical Big Data Analytics: Hands-on techniques to implement enterprise analytics and machine learning using Hadoop, Spark, NoSQL and R

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Practical Big Data Analytics

Big Data Mining for the Masses

Implementing a big data mining platform in an enterprise environment that serves specific business requirements is non-trivial. While it is relatively simple to build a big data platform, the novel nature of the tools present a challenge in terms of adoption by business-facing users used to traditional methods of data mining. This, ultimately, is a measure of how successful the platform becomes within an organization.

This chapter introduces some of the salient characteristics of big data analytics relevant for both practitioners and end users of analytics tools. This will include the following topics:

  • What is big data mining?
  • Big data mining in the enterprise:
    • Building a use case
    • Stakeholders of the solution
    • Implementation life cycle
  • Key technologies in big data mining:
    • Selecting the hardware stack:
      • Single/multinode architecture
      • Cloud-based...

What is big data mining?

Big data mining forms the first of two broad categories of big data analytics, the other being Predictive Analytics, which we will cover in later chapters. In simple terms, big data mining refers to the entire life cycle of processing large-scale datasets, from procurement to implementation of the respective tools to analyze them.

The next few chapters will illustrate some of the high-level characteristics of any big data project that is undertaken in an organization.

Big data mining in the enterprise

Implementing a big data solution in a medium to large size enterprise can be a challenging task due to the extremely dynamic and diverse range of considerations, not the least of which is determining...

Technical elements of the big data platform

Our discussion, so far, has been focused on the high-level characteristics of design and deployment of big data solutions in an enterprise environment. We will now shift attention to the technical aspects of such undertakings. From time to time, we’ll incorporate high-level messages where appropriate in addition to the technical underpinnings of the topics in discussion.

At the technical level, there are primarily two main considerations:

  • Selection of the hardware stack
  • Selection of the software and BI (business intelligence) platform

Over the recent 2-3 years, it has become increasingly common for corporations to move their processes to cloud-based environments as a complementary solution for in-house infrastructures. As such, cloud-based deployments have become exceedingly common and hence, an additional section on on-premises...

Summary

In this chapter, we got a high-level overview of Big Data and some of the components of implementing a Big Data solution in the Enterprise. Big Data requires selection of an optimal software and hardware stack, an effort that is non-trivial, not least because of the hundreds of solutions in the industry. Although the topic of a Big Data strategy may be deemed as a subject best left for management rather than a technical audience, it is essential to understand the nuances.

Note that without a proper, well-defined strategy and corresponding high level support, IT departments will remain limited in the extent to which they can provide successful solutions. Further, the solution, including the hardware-software stack should be such that it can be adequately managed and supported by existing IT resources. Most companies will find that it would be essential to recruit new hires...

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Key benefits

  • A perfect companion to boost your Big Data storing, processing, analyzing skills to help you take informed business decisions
  • Work with the best tools such as Apache Hadoop, R, Python, and Spark for NoSQL platforms to perform massive online analyses
  • Get expert tips on statistical inference, machine learning, mathematical modeling, and data visualization for Big Data

Description

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.

Who is this book for?

The book is intended for existing and aspiring Big Data professionals who wish to become the go-to person in their organization when it comes to Big Data architecture, analytics, and governance. While no prior knowledge of Big Data or related technologies is assumed, it will be helpful to have some programming experience.

What you will learn

  • Get a 360-degree view of the world of Big Data, data science, and machine learning
  • Go through a broad range of technical and business Big Data analytics topics that caters to the interests of technical experts as well as corporate IT executives
  • Get hands-on experience with industry-standard Big Data and machine learning tools such as Hadoop, Spark, MongoDB, kdb+, and R
  • Create production-grade machine learning BI dashboards using R and R Shiny with step-by-step instructions
  • Learn how to combine open-source Big Data, machine learning, an BI tools to create low-cost business analytics applications
  • Understand corporate strategies for successful Big Data and data science projects
  • Go beyond general-purpose analytics to develop cutting-edge Big Data applications using emerging technologies
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Length: 412 pages
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Table of Contents

12 Chapters
Too Big or Not Too Big Chevron down icon Chevron up icon
Big Data Mining for the Masses Chevron down icon Chevron up icon
The Analytics Toolkit Chevron down icon Chevron up icon
Big Data With Hadoop Chevron down icon Chevron up icon
Big Data Mining with NoSQL Chevron down icon Chevron up icon
Spark for Big Data Analytics Chevron down icon Chevron up icon
An Introduction to Machine Learning Concepts Chevron down icon Chevron up icon
Machine Learning Deep Dive Chevron down icon Chevron up icon
Enterprise Data Science Chevron down icon Chevron up icon
Closing Thoughts on Big Data Chevron down icon Chevron up icon
External Data Science Resources Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Purabi Dasgupta Dec 25, 2018
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I know now why this book has become the best seller in USA. In recent times no other book has such brilliant writing and informative. It has been explained in such easy ways that even people like me with limited knowledge about the subject didn’t have any problems understanding it. Brilliant no doubt.
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