The significant advances in high- throughput sequencing technologies over the last decade have led to an exponential explosion of biological data. Consequently, bioinformatics is encountering unprecedented challenges in data storage and analysis, memory allocation, computational power and time complexity. It is therefore becoming increasingly disturbing for small laboratories and some large institutions to establish and maintain infrastructures for data processing. This has forced bioinformatics to take a leap-forward from in- house computing to cloud computing to address these issues. In this paper, the authors thus explore the current applications of big data analytics in bioinformatics on the cloud. The findings in each in each use case reveal that cloud computing remarkably improve processing and storage of the continuously growing data in bioinformatics. Though there are some bottlenecks to solve, cloud computing promises to provide a lightweight environment to develop pipelines for prognosis, diagnosis, drug discovery and personalized medicine. The knowledge generated by this review will help scientist who are facing challenges with big data to resort to use cloud computing as an alternative solution and get results faster and without the need to install and maintain or update expensive software. This paper also serves to bring awareness to scientists on the current technologies that are used to manage data.