Book Image

Securing Hadoop

By : Sudheesh Narayan
Book Image

Securing Hadoop

By: Sudheesh Narayan

Overview of this book

Security of Big Data is one of the biggest concerns for enterprises today. How do we protect the sensitive information in a Hadoop ecosystem? How can we integrate Hadoop security with existing enterprise security systems? What are the challenges in securing Hadoop and its ecosystem? These are the questions which need to be answered in order to ensure effective management of Big Data. Hadoop, along with Kerberos, provides security features which enable Big Data management and which keep data secure. This book is a practitioner's guide for securing a Hadoop-based Big Data platform. This book provides you with a step-by-step approach to implementing end-to-end security along with a solid foundation of knowledge of the Hadoop and Kerberos security models. This practical, hands-on guide looks at the security challenges involved in securing sensitive data in a Hadoop-based Big Data platform and also covers the Security Reference Architecture for securing Big Data. It will take you through the internals of the Hadoop and Kerberos security models and will provide detailed implementation steps for securing Hadoop. You will also learn how the internals of the Hadoop security model are implemented, how to integrate Enterprise Security Systems with Hadoop security, and how you can manage and control user access to a Hadoop ecosystem seamlessly. You will also get acquainted with implementing audit logging and security incident monitoring within a Big Data platform.
Table of Contents (15 chapters)
Securing Hadoop
About the Author
About the Reviewers

Securing sensitive data in Hadoop

Sensitive data inside Hadoop can be classified into two high-level categories:

  • Sensitive data related to customers' personal information, customers' financial information, and so on that exists in enterprise systems and that needs to be brought to Hadoop for analysis.

  • The Hadoop analytical process generates sensitive insights after processing the data stored inside Hadoop. These insights are more valuable and sensitive compared to the raw source data that is used to generate them. For example, a retail e-commerce enterprise has detailed transactions of customer purchases. These transaction details might not be very sensitive. This data is brought to Hadoop for generating various insights. Using the customer historical purchases and correlating the same with customer's household purchases, insights related to customer purchase patterns, behavior patterns, customer sentiment, and customer life events could be inferred. This information is highly sensitive compared...