Abstract
This study provides an insight into one of digital forensics' needs by analysing big data. Digital forensics is one of the branches of forensic science specialised in recovering data from digital devices for investigation for purposes of computer crime or other goals. The study shows that the main challenges faced by digital investigators are those relating to the storage, management, and analysis of a large amount of data of various types, including organised and semi-organised. Investigators rely on specific tools to handle big data like Hadoop, Spark Apache, and SAS. Hadoop provides a system for storing massive files on distributed files and analysing their components, while Spark Apache provides quick analysis of distributed data without storing it. SAS visual analysis of big data provides fast support for data discovery and visualisation via a memory drive. An overview of these three big data technologies is reviewed through their components and the processes by which these features are compared and then compared. The study shows how the greatest benefit is achieved by bringing these tools together when using rather than relying on one and not the other.