Big Data & Hadoop

Big data technology is widely used for biomedical research and health-care informatics for generating and collecting a large amount of biological and clinical data. The technological development of big data includes the emergence of new sequencing machines, the development of parallel computing, and the expansion of EHRs will drastically reduce the cost associated with collecting and analyzing biomedical data. The four significant biomedical applications using big data are bioinformatics, imaging informatics, clinical informatics and public health informatics. However, bioinformatics used to facilitate high-throughput experiments associated with genome studies of disease; clinical informatics use big data to collect patient data for making intelligent decisions. Imaging informatics is used to integrate medical image data and finally, public health informatics used for predicting and monitoring infectious disease such as Ebola.

Hadoop is an open-source software framework which helps to store data and running applications on clusters of commodity hardware. Further, it provides massive storage for any kind of data with enormous processing power and can handle virtually limitless concurrent tasks. Hadoop is increasingly essential because of the ability to store and process huge amounts of data in an active phase, especially for social media and the Internet of Things (IoT). Hadoop process big data fast and the processing power can be adjusted by using more computing nodes. Further, data and application processing are protected as it takes multiple copies of all the data stored; thus, it has fault tolerance, flexibility, scalability and cost-effective. Every new technological development has its challenges to be answered, and there is always room for innovation and improvement. Such challenges include a map-reduce program which will not be a good match for all problems, knowledge gap, data security, data management and governance.