![]() Can you start by describing what is Materialize?įrank McSherry 00:01:38 Certainly. I’m delighted to be here.Īkshay Manchale 00:01:29 Frank, let’s get started with Materialize and set the context for the show. Frank, welcome to the show.įrank McSherry 00:01:27 Thanks very much, Akshay. He also did some work on differential privacy back in the day. Frank is the chief scientist at Materialize and prior to that, he did a fair bit of relatively public work on dataflow systems - first at Microsoft, Silicon Valley, and most recently ETH, Zurich. My guest today is Frank McSherry and we will be talking about Materialize. To suggest improvements in the text, please contact and include the episode number and URL.Īkshay Manchale 00:01:03 Welcome to Software Engineering Radio. This transcript was automatically generated. Transcript brought to you by IEEE Software magazine. GitHub – TimelyDataflow/differential-dataflow: An implementation of differential dataflow using timely dataflow on Rust.GitHub – TimelyDataflow/timely-dataflow: A modular implementation of timely dataflow in Rust.Timely Dataflow: Timely dataflow in three easy steps!.Eventual Consistency isn’t for Streaming – Materialize.Using Kafka as Your Primary Data Store? Here’s Why You Shouldn’t.Streaming SQL: What is it, why is it useful? – Materialize.The Streaming Database for Real-time Analytics.Episode 433: Episode 433: Jay Kreps on ksqlDB.Episode 456: Episode 456: Tomer Shiran on Data Lakes.Episode 473: Episode 473: Mike Del Balso on Feature Stores.Episode 370: Episode 370: Chris Richardson on Microservice Patterns.Episode 162: Episode 162: Project Voldemort with Jay Kreps.Episode 219 – Episode 219: Apache Kafka with Jun Rao.Episode 393 – Episode 393: Jay Kreps on Enterprise Integration Architecture with a Kafka Event Log.The conversation explores the differential/timely data flow that powers the compute plane of Materialize, how it timestamps data from sources to allow for incremental view maintenance, as well as how it’s deployed, how it can be recovered, and several interesting use cases. Host Akshay Manchale spoke with Frank about various ways in which analytical systems are built over streaming services today, pitfalls associated with those solutions, and how Materialize simplifies both the expression of analytical questions through SQL and the correctness of the answers computed over multiple data sources. Frank McSherry, chief scientist at Materialize, talks about the Materialize streaming database, which supports real-time analytics by maintaining incremental views over streaming data. ![]()
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