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 3. Building a Fraud Detection System

For fraud detection in financial systems, several commercial solutions are available on the market. However, Hadoop and its ecosystems of tools offer the opportunity to build a fraud detection system that can supplement existing fraud detection systems and lower their operational costs by downloading certain fraud detection tasks to Hadoop.

In this chapter, we have chosen Hadoop and Spark to build a simple fraud detection system. The following steps will be involved in building this solution:

  1. Selecting and cleansing the dataset.

  2. Designing the high-level architecture.

  3. Creating the fraud detection model.

  4. Putting the model to use.

You will learn how to build a machine learning model using Spark and Hadoop with the help of available datasets. You will use this model and Spark streaming to detect anomalies in real time that might be caused by fraudulent transactions.

In the upcoming sections of this chapter, we will cover how to build a basic fraud detection...