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

Putting the fraud detection model to use

A model can only be useful when it is put into operation. Typically, a data scientist or a team of data scientists will spend considerable time analyzing the raw data and build a machine learning model from it. Once a model is ready, it can be used on a web portal, workflow engine, or in a batch program to make predictions.

Our model is intended to detect anomalies in transaction data signaling suspicious transactions. Once we detect an anomalous transaction then we can take several measures to prevent the customer suffering a loss, such as putting a transaction on hold while waiting for the customer to confirm if it is valid by phone.

To put this model to use, we will create a data stream that simulates incoming real-time transactions. We should convert the incoming transactions into a form suitable for our fraud detection model created in the previous section. It means that the input to the model should be in the form 1, 3, 75, where 1 is the day...