Problem Statement: Small and Medium-Sized Enterprises (SMEs) play an integral role in the economy of developed and developing countries. SMEs are constantly searching for innovative technologies that will not only reduce their overhead costs but also improve product development, customer relations and profitability. Literature has revealed that some SMEs around the world have incorporated a fairly new technology called Big Data to achieve higher levels of operational efficiency. Therefore, it is interesting to observe the reasons why some organizations in developing countries such as South Africa are not adopting this technology as compared to other developed countries. A large portion of the available literature revealed that there isa general lack of in-depth information and understanding of Big Data amongst SMEs in developing countries such as South Africa. The main objective of this study is to explain the factors that SMEs consider during the Big Data decision process. Purpose of the study: This research study aimed to identify the factors that South African SMEs consider as important in their decision-making process when it comes to the adoption of BigData. The researcher used the conceptual framework proposed by Frambach and Schillewaert to derive an updated and adapted conceptual framework that explained the factors that SMEs consider when adopting Big Data. Research methodology: SMEs located in the Western Province of South Africa were chosen as the case studies. The interpretive research philosophy formed the basis of this research. Additionally, the nature of the phenomenon being investigated deemed it appropriate that the qualitative research method and research design be applied to this thesis. Due to constraints such as limited time and financial resources this was a cross-sectional study. The research strategy in this study was multiple in-depth case studies. The qualitative approach was deemed appropriate for this study. The researcher used two methods to collect data, namely, the primary research method and the secondary research method. The primary research method enabled the researcher to obtain rich data that could assist in answering the primary research questions, whilst the secondary research method included documents which supplemented the primary data collected. Data was analyzed using the NVivo software provided by the University of Cape Town. Key Findings: The findings suggest that the process that influences the decision to adopt Big Data by SMEs follows a three-step approach namely: 1.) Awareness, 2.) Consideration, 3.) Intention. This indicates that for Big Data to be adopted by SMEs there must be organizational readiness to go through the process. This study identified the main intention for SMEs to adopt Big Data is to ensure operational stability. Improved operational efficiency was identified as the supporting sub-theme. This study has raised awareness about the process that SMEs, academic researchers, IT practitioners and government need to place emphasis on to improve the adoption of Big Data by SMEs. Furthermore, this study has raised awareness about the opportunities and challenges that SMEs, academic researchers, IT practitioners and government need to place emphasis on to improve the adoption of Big Data by SMEs. Value of the study: The study adds value in both academia and the business industry as it provides more insight into the factors that SMEs consider in the Big Data adoption decision.

This work deals with the design of distributed repository aimed at storing digital forensic data. The theoretical part of the thesis describes digital forensics and what is its purpose. There are also explained Big data, suitable storages, their properties, advantages and disadvantages, in this part. The main part of the thesis deals with the design and implementation of distributed storage for digital forensic data. The design is also focused in suitable indexing of stored data, and supporting new types of digital forensic data. The performance of implemented system was evaluated for chosen type of digital forensic data PCAP files.