Book Image

Big Data Forensics: Learning Hadoop Investigations

Book Image

Big Data Forensics: Learning Hadoop Investigations

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

Application collection approaches


Hadoop data is stored in a unique structure. Unlike most relational database systems, which loads and stores data in a proprietary format, Hadoop applications typically store data in sets of flat files similar to a hierarchical database. Files are imported into the application, and the application stores those files in a separate file structure and generates the metadata about that data.

Application-based collections have advantages over filesystem-based collections of the application's underlying files. While the file-based storage of files in Hadoop applications enables logical copies of the flat files, these files may not be structured in a format that can be quickly analyzed or the collection may require sampling of files to identify the relevant files. Collecting data from the applications has the following advantages:

  • The investigator can collect the data in a format that is quickly and readily analyzable

  • The data can be collected more easily by third...