Streaming SQL databases can simplify how organizations work with streaming data sources like Apache Kafka. A complete SQL streaming database provides the powerful offering to developers of asking questions about streaming data, and then getting correct answers with millisecond latency, even when the underlying data changes rapidly - commonly called incremental view maintenance.

Data engineers build applications in streaming SQL databases using ANSI-standard SQL, so there is no need to develop custom code which significantly reduces costs and time-to-market.

In this presentation, Materialize Chief Scientist Frank McSherry will review the architectural details that distinguish how streaming SQL databases differ from traditional systems, demonstrate streaming data exploration using standard SQL, and showcase interactive, incrementally-maintained queries over continually-changing data sets. Materialize is based on Timely Dataflow and Differential Dataflow, providing a complete streaming SQL database solution with support for complex, multi-way JOINs on streaming data.

Frank McSherry

Co-Founder & Chief Scientist,
Materialize

Frank McSherry is co-founder and Chief Scientist at Materialize.

Prior to starting Materialize, Frank co-invented Differential Privacy and led the Naiad project. Frank is the primary author of Timely Dataflow and Differential Dataflow, the two open source projects that power Materialize.

Frank previously worked at Microsoft Research Silicon Valley, and holds a Ph.D in Computer Science from the University of Washington.