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Download Slides: https://www.datacouncil.ai/talks/the-materialize-incremental-view-maintenance-engine

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ABOUT THE TALK

At Materialize, Inc. we are building a high-throughput, low-latency SQL view maintenance engine. You write SQL queries against continually evolving relations, we give you back the answers fast. You ask the queries again and you get updated answers in milliseconds.

This system design departs fundamentally from both Spark-like and relational database systems, and is based instead on timely dataflow and differential dataflow. In this talk, we will go through the architectural highlights distinguish Materialize from prior systems, call out how they enable interactive queries over continually evolving data, and demonstrate the stack used for real-time data warehousing.

ABOUT THE SPEAKER

Frank McSherry is an independent researcher working on problems related to scalable distributed computation. He was part of Microsoft Research's Silicon Valley group where he jointly invented differential privacy, basked in the light of DryadLINQ, and then lead the Naiad project. Most recently he has been working on Rust implementations of Naiad's timely and differential dataflows in collaboration with ETH Zürich's Systems Group.