With the Big Data growing in the popularity, more and more architectures are being proposed, but no unified solution is at hand. The main objective of this thesis is to specify and prepare a conceptual measurement model on which selected Big Data Platform Architecture will be mapped and compared. The conceptual measurement model is split into multiple components with the specification of responsibilities, and the their scope of the conceptual measurement model is set. All architectures mapped in a single structure are then evaluated and compared. Our research contains a brief overview of the Big Data concept and a summary of technologies used in evaluated architectures, if available. In addition, social commerce is a new paradigm infrastructure model of commercial market, which use of Web 3.0 technologies and social data to support their exchange activities. While it is popular, being a subset of commercial market, has been increasing tremendously since its introduction in 2012, there exists a general particular of research on their framework and applications’ effectiveness, especially in areas beyond the general social commerce practices. This research develops a comprehensive social commerce framework in big data environment that has four key components. Then our research applies the Big Data Platform (BDP) to analyze the usability and adaptability with the framework by applying it to social commerce successful companies. Accordingly, we provide a set of metrics for social commerce-based Big Data Platform design. The proposed our framework and they are used to lead the design and evaluation in social commerce environment. This research makes a contribution to the social commerce in proposing a new design framework and also, we have to examine in providing inside for effective approach of concerning with their social business.