Abstract: Big data in biology generally refers to omics data generated by multiple high-throughput screening technologies, encompassing genomics, transcriptomics, proteomics, epigenomics, metabolomics, microbiomics, et al. Essentially, it can be regarded as the digital record of multi-layered biological activities derived from genetic information flow, and the responses of life systems to environmental changes. The explosive growth of omics data can be attributed to the rapid advances and widespread adoption of high-throughput sequencing technologies. Simultaneously, the deep integration and application of techniques from biology, mathematics, and computer science have enhanced the analytical and processing capabilities of biological big data. This integration has brought revolutionary impacts on the exploration, innovation, and clinical applications in modern biomedicine. This article primarily provides a review of the various omics data constituting biological big data and their applications in biomedicine, while also outlining future development directions.