This paper provides an overview of the application of big data in psychology, exploring its relevance, potential applications, and associated challenges. It begins with a presentation of the concept of big data, including its architectures and technologies, emphasizing their importance in psychological research. After that, the paper delves into a set of existing data in the literature, taking into account high-level criteria such as volume, variety, and velocity. These criteria are expanded to include additional standards like ethical considerations, acquisition velocity, and value. Following that, the paper explores various studies about the analysis and processing of massive datasets in psychology, with an emphasis on study objectives and techniques employed, such as data warehouses and parallel processing. Finally, the paper concludes with a discussion on the criticisms of the use of big data in psychology and the significance of integrating other techniques such as artificial intelligence (AI) and natural language processing (NLP). Overall, the present study highlights the potential of big data in advancing psychological research, while acknowledging the need to address associated challenges and adopt complementary methodologies.