SQL-like big data environments: Case study in clinical trial analytics

Abstract

Big Data deals with enormous volumes of complex and exponentially growing data sets from multiple sources. With rapid growth in technology, we are now able to generate immense amount of data in almost any field imaginable including physical, biological and biomedical sciences. With the diversity and amount of data in health care industry there is an increasing need to evaluate the components in big data frameworks and gauge their adaptability to analytics techniques. However, a key step in adapting big data tools is the portability of relational databases to big data environment. Since SQL is considered to be the de-facto language for interactive queries, in this paper, we evaluate the performance of SQL-like big data solutions for the portability of existing relational databases. Our work focuses on benchmarking multiple SQL-like big data technologies over Hadoop based distributed file system (HDFS) for Study Data Tabulation Model (SDTM) used in clinical trial databases for improving the efficiency of research in clinical trials. We use publically available clinical trial data (from National Institute on Drug Abuse (NIDA)), which follows SDTM, as a test bed to measure key parameters like usability, adaptability, modularity, robustness and efficiency of these solutions. With the intention to demonstrate how current clinical trial functionality can be replicated on a big data backend with high SQL-like functionality, we evaluate several types of ad-hoc SQL queries.