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With Frank McSherry (Co-Founder & Chief Scientist, Materialize).
Tuesday, October 20, 2020 at 06:30 PM   Absolutely Free   Online

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VIRTUAL-EVENT We will give away a whole bunch of free T-Shirts at this virtual-event!

Presented by Frank McSherry, Co-Founder & Chief Scientist, Materialize

REQUIRED to attend register now while there is still space:

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, & 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 & 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, & showcase interactive, incrementally-maintained queries over continually-changing data sets. Materialize is based on Timely Dataflow & 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 & Chief Scientist at Materialize (

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

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

Register now while there is still space:

Virtual Power-Networking at 6:30pm
Freebie swag giveaway at 6:35pm
Presentation starts at 6:40pm

A Database Month event

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