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Storing and processing data at different locations using a heterogeneous set of formats and data managements systems is state-of-the-art in many organizations. However, data analyses can often provide better insight when data from several sources is integrated into a combined perspective. In this paper we present an overview of our data integration system DataCalc. DataCalc is an extensible integration platform that executes adhoc analytical queries on a set of heterogeneous data processors. Our novel platform uses an expressive function shipping interface that promotes local computation and reduces data movement between processors. In this paper, we provide a discussion of the overall architecture and the main components of DataCalc. Moreover, we discuss the cost of integrating additional processors and evaluate the overall performance of the platform.

Many organizations store and process data at different locations using a heterogeneous set of formats and data management systems. However, data analyses can often provide better insight when data from several sources is integrated into a combined perspective. DataCalc is an extensible data integration platform that executes ad-hoc analytical queries on a set of heterogeneous data processors. The platform uses an expressive function shipping interface that promotes local computation and reduces data movement between processors. In this paper, we provide a detailed discussion of the architecture and implementation of DataCalc. We introduce data processors for plain files, JDBC, the MongoDB document store, and a custom in memory system. Finally, we discuss the cost of integrating additional processors and evaluate the overall performance of the platform. Our main contribution is the specification and evaluation of the DataCalc code delegation interface.