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Big Data Forensics: Learning Hadoop Investigations

Big Data Forensics: Learning Hadoop Investigations

By : Joe Sremack
5 (3)
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Big Data Forensics: Learning Hadoop Investigations

Big Data Forensics: Learning Hadoop Investigations

5 (3)
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)
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9
Index

What this book covers

Chapter 1, Starting Out with Forensic Investigations and Big Data, is an overview of both forensics and Big Data. This chapter covers why Big Data is important, how it is being used, and how forensics of Big Data is different from traditional forensics.

Chapter 2, Understanding Hadoop Internals and Architecture, is a detailed explanation of Hadoop's internals and how data is stored within a Hadoop environment.

Chapter 3, Identifying Big Data Evidence, covers the process for identifying relevant data within Hadoop using techniques such as interviews, data sampling, and system reviews.

Chapter 4, Collecting Hadoop Distributed File System Data, details how to collect forensic evidence from the Hadoop Distributed File System (HDFS) using physical and logical collection methods.

Chapter 5, Collecting Hadoop Application Data, examines the processes for collecting evidence from Hadoop applications using logical- and query-based methods. HBase, Hive, and Pig are covered in this chapter.

Chapter 6, Performing Hadoop Distributed File System Analysis, details how to conduct a forensic analysis of HDFS evidence, utilizing techniques such as file carving and keyword analysis.

Chapter 7, Analyzing Hadoop Application Data, covers how to conduct a forensic analysis of Hadoop application data using databases and statistical analysis techniques. Topics such as Benford's law and clustering are discussed in this chapter.

Chapter 8, Presenting Forensic Findings, shows to how to present forensic findings for internal investigations or legal proceedings.

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