Rapid Adoption of Cloud Data Warehouse Technology Using Datometry Hyper-Q
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. Enterprises eager to move to the cloud face a significant dilemma: making existing applications work with new database platforms is an enormously costly undertaking that calls for rewriting and adjusting of 100’s if not 1,000’s of applications. In this talk, we present a next-generation virtualization technology that lets existing applications run natively on cloud-based database systems. Using this platform, enterprises can move rapidly to the cloud and innovate and create competitive advantage as a matter of months instead of years. We describe technology and application scenarios and demonstrate effectiveness and performance of the approach through actual customer use cases.
Lyublena Antova is a Research Scientist at Datometry, Inc., with expertise in the areas of query optimization, large-scale database systems, and distributed systems and is a founding member of the team that designed and built the Orca query optimizer at Greenplum/EMC/Pivotal. Lyublena received her B.S. in Computer Science from Sofia University St. Kliment Ohridski (Bulgaria), her M.S. in Computer Science from Saarland University (Germany), and her Ph.D. in Computer Science from Cornell University.
Mohamed Soliman, Chief Architect
Mohamed is Datometry’s Chief Architect with deep expertise in distributed data processing,
query processing, data warehousing. Prior to Datometry, Mohamed was a Staff Engineer
at Pivotal Inc. where he worked on building Greenplum’s Orca query optimizer initiative.
Mohamed received his B.S. and M.S. in Computer Science from Alexandria University
(Egypt), and his Ph.D. in Computer Science from the University of Waterloo, Canada.