Enterprises worldwide are looking to move their database applications to the cloud. However, conventional migration from an on-premise data warehouse to a cloud-native one is a costly, labor-intensive task, laden with many risks. According to Gartner, the majority of these migrations are late, run over budget, or fail altogether.
Datometry has developed a virtualization platform that enables applications written for an on-premises data warehouse to run on a cloud data warehouse — without major rewrites, without rearchitecting. Instead, Datometry Hyper-Q emulates all missing or diverging features in real-time. Compared to manual and/or tool-aided rewrites, virtualization has higher expressivity and can address the full spectrum of language incompatibilities.
In this talk, we present the overall architecture of such a virtualization platform and the specific challenges it needs to overcome. We will discuss prominent feature groups and the different levels of emulation they require. And, of course, we will answer the frequently asked question “is there anything virtualization cannot do?”
Lyublena Antova is a Senior Research Scientist at Datometry, Inc., with expertise in the areas of query optimization, metadata management and large-scale database systems. Previously, she worked at Greenplum as a founding member of the team that designed and built the Orca query optimizer. Lyublena received her B.S. in Computer Science from Sofia University (Bulgaria), her M.S. in Computer Science from Saarland University (Germany), and her Ph.D. in Computer Science from Cornell University (USA).
This talk is part of the Quarantine Database Tech Talk Seminar Series.
Zoom Participation. See announcement.