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Big Data Forensics: Learning Hadoop Investigations
Big Data Forensics: Learning Hadoop Investigations

Big Data Forensics: Learning Hadoop Investigations: Perform forensic investigations on Hadoop clusters with cutting-edge tools and techniques

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Big Data Forensics: Learning Hadoop Investigations

Chapter 2. Understanding Hadoop Internals and Architecture

Hadoop is currently the most widely adopted Big Data platform, with a diverse ecosystem of applications and data sources for forensic evidence. An Apache Foundation framework solution, Hadoop has been developed and tested in enterprise systems as a Big Data solution. Hadoop is virtually synonymous with Big Data and has become the de facto standard in the industry.

As a new Big Data solution, Hadoop has experienced a high adoption rate by many types of organizations and users. Developed by Yahoo! in the mid-2000s—and released to the Apache Foundation as one of the first major open source Big Data frameworks—Hadoop is designed to enable the distributed processing of large, complex data sets across a set of clustered computers. Hadoop's distributed architecture and open source ecosystem of software packages make it ideal for speed, scalability, and flexibility. Hadoop's adoption by large-scale technology...

The Hadoop architecture

Hadoop is a reliable system for shared storage and analysis with a rich ecosystem of layered solutions and tools for Big Data. Hadoop is built on the concepts of distribution for storage and computing. It is a cross-platform, Java-based solution. Hadoop can run on a wide array of different operating systems, such as Linux and Windows, because it is built in Java, a platform-neutral language. Hadoop itself is a layer that sits on top of the host operating system. Hadoop's core functionalities are also built in Java and can be run as separate processes. With its own filesystem and set of core functionalities, Hadoop serves as its own abstract platform layer; it can be accessed and run almost entirely independent of the host operating system.

The following figure shows a high-level representation of the Hadoop layers:

The Hadoop architecture

Figure 1: The Hadoop architecture layers

The Hadoop layers are an abstraction for how the various components are organized and the relationship between...

Hadoop data analysis tools

Hadoop was designed to store and analyze large volumes of data. The ecosystem of tools for Hadoop analysis is large and complex. Depending on the type of analysis, many different tools can be used. The Apache Foundation set of tools has a number of standard options such as Hive, HBase, and Pig, but other open source and commercial solutions have been developed to meet different analysis requirements using Hadoop's HDFS and MapReduce features. For example, Cloudera's Impala database runs on Hadoop, but it is not part of the Apache Foundation suite of applications.

Understanding which data analysis tools are used in a Hadoop cluster is important for identifying and properly collecting data. Some data analysis tools store data in formatted files and may offer easier methods for data collection. Other tools may read data directly from files stored in HDFS, but the scripts used for the tool may serve as useful information when later analyzing the data. This...

Managing files in Hadoop

Hadoop has its own file management concepts that come with many different mechanisms for data storage and retrieval. Hadoop is designed to manage large volumes of data distributed across many nodes built with commodity hardware. As such, Hadoop manages the distribution of large volumes of data using techniques designed to divide, compress, and share the data all while dealing with the possibilities of node failures and numerous processes accessing the same data simultaneously. Many of the filesystem concepts in Hadoop are exactly the same as in other systems, such as directory structures. However, other concepts, such as MapFiles and Hadoop Archive Files, are unique to Hadoop. This section covers many of the file management concepts that are unique to Hadoop.

File permissions

HDFS uses a standard file permission approach. The three types of permissions for files and directories are:

  • Read (r): Read a file and list a directory's contents
  • Write (w): Write to a file...

The Hadoop forensic evidence ecosystem

Forensics is based on evidence. For digital investigations, evidence is data. For Hadoop, the evidence is the information stored on disk and in memory. Not all information stored in Hadoop is relevant; it depends on the nature of the investigation. Evidence that is relevant in one investigation may not be relevant in another. This section summarizes the various sources of evidence and the overall ecosystem of Hadoop forensic evidence.

Standard Hadoop processes or system-generated diagnostic information may not be relevant to a forensic investigation. For example, a Hadoop cluster installed without any customizations that only stores and analyses web log data may not require a collection of all files and process data. Instead, a targeted collection of the web log data can be performed without losing evidence. In other investigations, collecting the log and configuration files may be necessary.

Forensic data in Hadoop falls into three categories:

  • Supporting...

Running Hadoop

Hadoop can be run from a number of different platforms. Hadoop can be installed and run from a single desktop, from a distributed network of systems, or as a cloud-based service. Investigators should be aware of the differences and versed in the various architectures. Hadoop runs in the same manner on all three setups; however, collecting evidence may require different steps depending on how the data is stored. For instance, a cloud-based Hadoop server may require a different collection because of the lack of physical access to the servers.

This section details how to set up and run Hadoop using a free virtual machine instance (LightHadoop) and a cloud-based service (Amazon Web Services). Both LightHadoop and Amazon Web Services are used in the examples throughout this book. They serve as testbed environments to highlight how Big Data forensics is performed against different setups.

LightHadoop

Many of the examples in this book are intended to be hands-on exercises using LightHadoop...

The Hadoop architecture


Hadoop is a reliable system for shared storage and analysis with a rich ecosystem of layered solutions and tools for Big Data. Hadoop is built on the concepts of distribution for storage and computing. It is a cross-platform, Java-based solution. Hadoop can run on a wide array of different operating systems, such as Linux and Windows, because it is built in Java, a platform-neutral language. Hadoop itself is a layer that sits on top of the host operating system. Hadoop's core functionalities are also built in Java and can be run as separate processes. With its own filesystem and set of core functionalities, Hadoop serves as its own abstract platform layer; it can be accessed and run almost entirely independent of the host operating system.

The following figure shows a high-level representation of the Hadoop layers:

Figure 1: The Hadoop architecture layers

The Hadoop layers are an abstraction for how the various components are organized and the relationship between the...

Left arrow icon Right arrow icon

Key benefits

  • Identify, collect, and analyze Hadoop evidence forensically
  • Learn about Hadoop’s internals and Big Data file storage concepts
  • A step-by-step guide to help you perform forensic analysis using freely available tools

Description

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.

Who is this book for?

This book is meant for statisticians and forensic analysts with basic knowledge of digital forensics. They do not need to know Big Data Forensics. If you are an IT professional, law enforcement professional, legal professional, or a student interested in Big Data and forensics, this book is the perfect hands-on guide for learning how to conduct Hadoop forensic investigations. Each topic and step in the forensic process is described in accessible language.

What you will learn

  • Understand Hadoop internals and file storage
  • Collect and analyze Hadoop forensic evidence
  • Perform complex forensic analysis for fraud and other investigations
  • Use stateoftheart forensic tools
  • Conduct interviews to identify Hadoop evidence
  • Create compelling presentations of your forensic findings
  • Understand how Big Data clusters operate
  • Apply advanced forensic techniques in an investigation, including file carving, statistical analysis, and more

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Aug 24, 2015
Length: 264 pages
Edition : 1st
Language : English
ISBN-13 : 9781785288104
Vendor :
Apache
Category :
Languages :
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Tools :

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Product Details

Publication date : Aug 24, 2015
Length: 264 pages
Edition : 1st
Language : English
ISBN-13 : 9781785288104
Vendor :
Apache
Category :
Languages :
Concepts :
Tools :

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Table of Contents

9 Chapters
1. Starting Out with Forensic Investigations and Big Data Chevron down icon Chevron up icon
2. Understanding Hadoop Internals and Architecture Chevron down icon Chevron up icon
3. Identifying Big Data Evidence Chevron down icon Chevron up icon
4. Collecting Hadoop Distributed File System Data Chevron down icon Chevron up icon
5. Collecting Hadoop Application Data Chevron down icon Chevron up icon
6. Performing Hadoop Distributed File System Analysis Chevron down icon Chevron up icon
7. Analyzing Hadoop Application Data Chevron down icon Chevron up icon
8. Presenting Forensic Findings Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Full star icon 5
(3 Ratings)
5 star 100%
4 star 0%
3 star 0%
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1 star 0%
David Sep 25, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The first comment is: read correctly the title ;). A book on using big data for forensic would be great but that book is on doing forensic on big data :-D. Still, it is a topic of interest for people doing forensic and probably a must-have whenever you are confronted to the case. I suspect that this would happen once in every forensic analyst life and probably more and more as data goes to "cloud" and "big-data" storage.The book itself start with a description of Hadoop which is use for the book and on general principles. Then it cover more in details what you can expect to do and recover out of an Hadoop instance. I highly suspect that you could generalise and take profit of the work of the author for author big data systems even if not the primary goal of this book.As a con, I would say that there is a lot of text and discussion in the first half of the book and not enough examples and cheat sheet. If you need to start quickly, probably you could jump to the 2nd half of the book. Be careful that if you don't fully understand underlying concept you could potentially make a mistake and destroy your case.The author take the risk of covering the topic and it's nice. In a futur release or in a side book maybe he can cover more techniques, examples and demos but the book has the merit to exist and it contains a lot of good work!
Amazon Verified review Amazon
Winston Sep 29, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Big Data is all the rage today. Each day consumers are leaving big data footprints whether it is from internet use or other distributed computer systems. This book answers the question of how does one go about analyzing big data in a useful way.Note: This book is all about using Hadoop for forensics analysis of Big Data not just Big Data use cases.
Amazon Verified review Amazon
javier vicho soto Oct 09, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great book. Really interesting, both if you're working in forensic roles or you're a data professional working with hdfs / hadoop.You will have a lot of useful information in case of disaster recovery by example, in a way that is easy to extract all the relevant info from hdfs or hadoop applications. Very interesting the section about the deep analysis on HDFS, and the most useful tools available.In resume, amazing ebook if you want to know how to perform a deep and reliable investigation on Big Data side.
Amazon Verified review Amazon
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