Book Image

Hadoop Blueprints

By : Anurag Shrivastava, Tanmay Deshpande
Book Image

Hadoop Blueprints

By: Anurag Shrivastava, Tanmay Deshpande

Overview of this book

If you have a basic understanding of Hadoop and want to put your knowledge to use to build fantastic Big Data solutions for business, then this book is for you. Build six real-life, end-to-end solutions using the tools in the Hadoop ecosystem, and take your knowledge of Hadoop to the next level. Start off by understanding various business problems which can be solved using Hadoop. You will also get acquainted with the common architectural patterns which are used to build Hadoop-based solutions. Build a 360-degree view of the customer by working with different types of data, and build an efficient fraud detection system for a financial institution. You will also develop a system in Hadoop to improve the effectiveness of marketing campaigns. Build a churn detection system for a telecom company, develop an Internet of Things (IoT) system to monitor the environment in a factory, and build a data lake – all making use of the concepts and techniques mentioned in this book. The book covers other technologies and frameworks like Apache Spark, Hive, Sqoop, and more, and how they can be used in conjunction with Hadoop. You will be able to try out the solutions explained in the book and use the knowledge gained to extend them further in your own problem space.
Table of Contents (14 chapters)
Hadoop Blueprints
About the Authors
About the Reviewers

Chapter 4.  Marketing Campaign Planning

In this chapter, we will cover batch analytics using Hadoop. Traditional marketing campaigns which involve direct mailing to the entire customer base are an expensive project for companies. A marketer does not know in advance who is going to respond to a marketing campaign. Generally, the response to a campaign is considered in the form of an action that we expect the recipient to take as the outcome of a campaign. If a campaign fails to evoke the expected or desired response then it is not considered a successful campaign.

In this chapter, we will use Hadoop to perform batch analytics on a customer database to increase the likelihood of customer response in a marketing campaign. We will follow the following steps towards building the solution:

  • Understanding of classification as a supervised learning method

  • Building a machine learning model using historical response data

  • Using the machine learning model in a MapReduce job to generate a list of customers...