The database industry is about to undergo a fundamental transformation of unprecedented magnitude as enterprises start trading their well-established database stacks on premises for cloud database technology in order to take advantage of the economics cloud service providers have long promised. Industry experts and analysts expect the next years to prove a watershed moment in this transformation, as cloud databases finally reached critical mass and maturity.

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.