Wall Street's trading engines are complex database applications written for time series databases like kdb+ that uses the query language Q to perform real-time analysis. Extending the models to include other data sources, e.g., historic data, is critical for backtesting and compliance. However, Q applications cannot run directly on SQL databases. Therefore, financial institutions face the dilemma of either maintaining two separate application stacks, one written in Q and the other in SQL, which means increased IT cost and increased risk, or migrating all Q applications to SQL, which results in losing the inherent competitive advantage on Q real-time processing. Neither solution is desirable as both alternatives are costly, disruptive, and suboptimal. In this paper we present Hyper-Q, a data virtualization plat- form that overcomes the chasm. Hyper-Q enables Q applications to run natively on PostgreSQL-compatible databases by translating queries and results on the fly. We outline the basic concepts, detail specific difficulties, and demonstrate the viability of the approach with a case study.
['Lyublena Antova', 'Rhonda Baldwin', 'Derrick Bryant', 'Tuan Cao', 'Michael Duller', 'John Eshleman', 'Zhongxian Gu', 'Entong Shen', 'Mohamed A. Soliman', 'F. Michael Waas']
Datometry Hyper-Q: Bridging the Gap Between Real-Time and Historical Analytics