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

Hadoop security

When it was first created, Hadoop was not designed to work as the repository of an enterprise's entire store of data, as the data lake concept proposes. It was assumed that Hadoop will be operated in the trusted environment by the trusted users. Moreover, the early versions of Hadoop were used to store the data from public web logs, the confidentiality of which was not an issue. As Hadoop started getting positioned as the platform for enterprises, the Hadoop security concerns came to the forefront. To address these concerns, open source and proprietary solutions came on the market. These solutions focused upon a single security aspect such as data encryption and perimeter security, however they did not offer the fine-grained authorization on the data stored in Hadoop. A detailed discussion on Hadoop security is available at

HDFS permissions model

In our data lake use case, HDFS is the storage system for raw data. HDFS organizes...