Title: Commoditizing Data Analytics in the Cloud
Data analytics is moving into the cloud and many highly successful commercial cloud-native systems exist. However, existing systems are either expensive, lock in user data, or do both. In this talk, I will present ideas for how to commoditize large-scale data analytics in the cloud. Commoditization involves (a) reducing query processing cost to the theoretical hardware performance limits and (b) avoiding vendor lock-in by making it easy to move data between different clouds and systems. The architecture of an open and cost-efficient data analytics system can be split into three main components: First, an intelligent control component that automatically and transparently selects and manages the cheapest hardware instances for the given workload and makes migration to other cloud vendors possible. Second, a highly-efficient and scalable query processing engine that is capable of fully exploiting modern cloud hardware. Third, a data lake storage abstraction using open data formats that enables cheap storage as well as modularity and interoperability across different data systems.
Viktor Leis is a Professor of Computer Science at the Technical University of Munich (TUM). His research revolves around designing high-performance data management systems and includes core database systems topics such as query processing, query optimization, transaction processing, index structures, and storage. Another major research area is designing cloud-native, cost-efficient information systems. Viktor Leis received his doctoral degree in 2016 from TUM and was a professor at the Friedrich-Schiller-Universität Jena and Friedrich-Alexander-Universität Erlangen-Nürnberg before returning to TUM in 2022. He received the ACM SIGMOD dissertation award, the IEEE TCDE Rising Star Award, best paper awards at IEEE ICDE and ACM SIGMOD, and an ERC Starting Grant.
Tianyu Li is inviting you to a scheduled Zoom meeting.
Topic: Talk: Commoditizing Data Analytics in the Cloud
Time: Nov 29, 2023 01:00 PM Eastern Time (US and Canada)
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