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

Setting the up the solution architecture

The solution architecture for the marketing campaign system is essentially based on a batch process in which several enterprise information systems will participate. The solution has been described in Figure 2. The solution architecture can be divided into two major building blocks as follows:

  • Building the model: Building the model involves preparing the historical data and using it to build the model. It rarely happens that the historical data is clean enough for consumption by our model-building algorithms. Therefore, building the model also involves the preparation of data beforehand.

  • Scoring: The scoring process takes the new data for which we want to predict the outcome as input and predicts the value of the outcome. Scoring will tell us whether a person is likely to respond to a folder or not. Therefore, we can send the folders only to those who have a high likelihood of responding to our campaign. This will give us the opportunity to save the...