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

Collecting Hive evidence


Hive is a platform for analyzing data. It uses a familiar SQL querying language, so there is no need to write Java code for MapReduce functions. Hive operates such as a database and stores all metadata in a database, so accessing the database via queries should be familiar to people who have experience working with relational databases. Hive has several important components that are critical to understand for investigations:

  • Hive Data Storage: The type and location of data stored and accessed by Hive, which includes HDFS, Amazon S3, and other locations

  • Metastore: The database that contains Hive data metadata (not in HDFS)

  • HiveQL: The Hive query language, which is a SQL-like language

  • Databases and Tables: The logical containers of Hive data

  • Hive Shell: The shell interpreter for HiveQL

  • Hive Clients: The mechanisms for connecting a Hive server, such as Hive Thrift clients, Java Database Connectivity (JDBC) clients, and ODBC clients

Hive stores record-based data in files...