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

Configuring Kerberos for Hadoop ecosystem components

The Hadoop ecosystem is growing continuously and maturing with increasing enterprise adoption. In this section, we look at some of the most important Hadoop ecosystem components, their architecture, and how they can be secured.

Securing Hive

Hive provides the ability to run SQL queries over the data stored in the HDFS. Hive provides the Hive query engine that converts Hive queries provided by the user to a pipeline of MapReduce jobs that are submitted to Hadoop (JobTracker or ResourceManager) for execution. The results of the MapReduce executions are then presented back to the user or stored in HDFS. The following figure shows a high-level interaction of a business user working with Hive to run Hive queries on Hadoop:

There are multiple ways a Hadoop user can interact with Hive and run Hive queries; these are as follows:

  • The user can directly run the Hive queries using Command Line Interface (CLI). The CLI connects to the Hive metastore using...