Abstract: Big data has permeated various spheres, becoming ubiquitous across academia, business, and media. However, the lack of a universally accepted definition has led to conflicting interpretations, hindering effective communication among stakeholders grappling with vast data. This study aims to combine several definitions of big data into a coherent and understandable whole. It alludes to large datasets with diverse structures, sizes, and levels of complexity that pose difficulties for analysis, storage, and display. Big data analysis involves mining these datasets for hidden patterns and correlations, offering organizations a deeper and broader understanding to gain a competitive edge. The transformative potential of software-defined networking (SDN) and big data, which have attracted much interest from the academic and business communities, is undeniable. Their convergence holds promise for simplifying data acquisition, transmission, storage, and processing, opening a new era of possibilities. However, it is essential to note that big data also presents significant challenges, such as scheduling, data supply, optimization, processing in cloud data centers, and scientific designs, which are examined in this article. It draws attention to how well SDN can manage these difficulties. Furthermore, the paper examines the latest applications of Internet of Things (IoT)-based embedded systems, such as home automation and modern vehicle management, in the context of SDN-enabled IoT and embedded systems.