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

Designing the high-level architecture

The high-level architecture of the system that we are planning to build contains two parts. The first part is the model, which we will build using the historical transaction data. Once this model has been created, we will use it in the second part with new data to determine whether a particular transaction falls in a cluster of suspicious transactions.

Figure 1 Design of a fraud detection system

In the previous section, we have already cleansed the transaction history file to make it suitable for the machine learning algorithm. For building the model, we will use Apache Spark.

Introducing Apache Spark

Apache Spark is an open source, big data processing framework, developed in 2009 in UC Berkeley's AMPLab. It has been developed around the goals of delivering speed, ease of use, and sophisticated analytics.

It was open sourced in 2010 as an Apache project and it has now become one of the most active projects among Apache Software Foundation projects. The...