Query Languages for Polystore Databases for Large Scientific Data Archives

Recently, the database research community faces a challenge of managing a large amount of heterogeneous data. Various scientific data archives are using different techniques to handle such data efficiently. Like many other scientific domains, astronomy also has data archives which consist of a large amount of data, different data models and a variety of data types. Images, texts, key-and-values, and graphs make up the enormous volume of data available in the astronomical domain. Managing such data in a single database may have scalability, growth and performance issues. Thus, in this paper, we propose to demonstrate a prototype system to manage such heterogeneous data with multiple databases using the Polystore database approach. The prototype supports a set-theoretic query language for access to cloud-based data resources.