Paper Info
Title
Datometry Hyper-Q: Bridging the Gap Between Real-Time and Historical Analytics.
Abstract
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.
Year
DOI
Venue
2016
10.1145/2882903.2903739
SIGMOD Conference
Field
DocType
Citations 
SQL,Data mining,Query language,Data analysis,Computer science,Competitive advantage,Query by Example,Data virtualization,Analytics,Big data,Database
Conference
1
PageRank 
References 
Authors
0.37
6
10
Name
Order
Citations
PageRank
Lyublena Antova153523.19
Rhonda Baldwin2261.72
Derrick Bryant310.37
Tuan Cao410.71
Michael Duller5446.16
John Eshleman680.85
Zhongxian Gu7362.64
Entong Shen8261.38
Mohamed A. Soliman9364.66
F. Michael Waas1010.37