Book Image

Big Data Forensics: Learning Hadoop Investigations

By : Joe Sremack
Book Image

Big Data Forensics: Learning Hadoop Investigations

By: Joe Sremack

Overview of this book

Big Data forensics is an important type of digital investigation that involves the identification, collection, and analysis of large-scale Big Data systems. Hadoop is one of the most popular Big Data solutions, and forensically investigating a Hadoop cluster requires specialized tools and techniques. With the explosion of Big Data, forensic investigators need to be prepared to analyze the petabytes of data stored in Hadoop clusters. Understanding Hadoop’s operational structure and performing forensic analysis with court-accepted tools and best practices will help you conduct a successful investigation. Discover how to perform a complete forensic investigation of large-scale Hadoop clusters using the same tools and techniques employed by forensic experts. This book begins by taking you through the process of forensic investigation and the pitfalls to avoid. It will walk you through Hadoop's internals and architecture, and you will discover what types of information Hadoop stores and how to access that data. You will learn to identify Big Data evidence using techniques to survey a live system and interview witnesses. After setting up your own Hadoop system, you will collect evidence using techniques such as forensic imaging and application-based extractions. You will analyze Hadoop evidence using advanced tools and techniques to uncover events and statistical information. Finally, data visualization and evidence presentation techniques are covered to help you properly communicate your findings to any audience.
Table of Contents (10 chapters)
9
Index

Identifying evidence

Identifying evidence is a complex process. It involves surveying a set of possible sources of evidence and determining which sources warrant collection. Data in any organization's systems is rarely well organized or documented. Investigators will need to take a set of investigation requirements and determine which data needs to be collected. This requires the following steps:

  • Properly reviewing system and data documentation
  • Interviewing staff
  • Locating backup and noncentralized data repositories
  • Previewing data

The process of identifying Big Data evidence is made difficult by the large volume of data, distributed filesystem, the numerous types of data, and the potential for large-scale redundancy in evidence.

Big Data solutions are also unique since evidence can reside in different layers within it. Within Hadoop, evidence can take on multiple forms, as described in Chapter 2, Understanding Hadoop Internals and Architecture. To properly identify the evidence in Hadoop...