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

A business case for churn detection

Telecom companies lose more than 30% of customers annually as a result of customer churn in the US and Europe. The cost of acquiring a new customer is eight times than that of retaining an existing customer. This makes a strong business case for churn detection, a task which is ideal for Hadoop.

Analyzing telecom data with Hadoop to detect customer churn possess a unique set of challenges that stem from the massive datasets that need to be transformed and analyzed. The storage of this data is expensive due to its sheer volume, and the pre-processing of raw data before analysis is a time- and computing-intensive task. Hadoop offers low-cost storage for data processing, and it can efficiently deal with structured, semi-structured, and unstructured datasets, which makes Hadoop a useful technology for churn prediction.

In this chapter, we will use Hadoop MapReduce to analyze the data so that we can predict which customers are likely to churn. In order to do...