Self-compacting dataflows Those of you familiar with dataflow processing are likely also familiar with the constant attendant anxiety: won’t my constant stream of input data accumulate and eventually overwhelm my system? That’s a great worry! In many cases tools like differential dataflow work hard to maintain a compact representation for your data. At the same […]
In-order reliable message delivery is not enough. Showing views over streams of data requires thinking through additional consistency semantics to deliver correct results.
“Upserts” are a common way to express streams of changing data, especially in relational settings with primary keys. However, they aren’t the best format for working with incremental computation. We’re about to learn why that is, how we deal with this in differential dataflow and Materialize, and what doors this opens up! This post is […]
Materialize lets you ask questions about your streaming data and get fresh answers with incredibly low latency. Since announcing Materialize’s v0.1, we’ve briefly covered its functionality and how one might use it at a high level. Today we’re going to get a little more granular and talk about Materialize’s internals and architecture. The Big Picture […]
Trying out Materialize This post will also be available at my personal blog. We all at Materialize are working from home, and while this is all a bit weird and different, it gives me some time to write a bit more and try and show off some of what we have been up to. I […]
It’s with a great deal of excitement, and some trepidation, that we are now able to show off what we have been working on at Materialize. The Materialize product is a “streaming data warehouse”, which is a class of product you might not be familiar with as, to the best of our knowledge, there wasn’t […]