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
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface

Building a churn predictor using Hadoop


In this section, we will apply the knowledge gained in the previous section to build a churn predictor that can scale up for use with very large datasets. Hadoop is particularly suited to the processing of very large datasets.

Synthetic data generation tools

To build our churn prediction using Hadoop, we will work with a synthetic dataset. Synthetic datasets are good for testing and learning purposes because they do not carry the risk of personal information leakage, and they are simple to obtain or create. But they also carry the limitation that, unlike a real-life dataset, they never contain the truth. Synthetic data offers a good alternative to real life datasets if you want to get started quickly.

Some synthetic data generation techniques and tools are covered as follows:

Note

Synthetic Data Generation

To generate synthetic datasets, you can use a site such as http://www.generatedata.com/, which offers the generation of 100 rows in a dataset for free...