Some thoughts on replacing read replicas with replicas that use incremental view maintenance. They can improve DB stability, massively accelerate complex queries, and cut costs.

Data-intensive applications and AI agents need fresh, trustworthy, and transformed data—but pulling this off cost-effectively is a huge challenge. Materialize 25.2 tackles this head-on. Learn more about the release here:

Enterprise data infra is breaking under the weight of AI agents. MCP gives agents access to services—including databases—but most systems can’t handle the load. Materialize’s MCP server turns live data products into tools agents can use—without crushing your systems or.

RT @withdelphi: How do you scale to handle thousands of real-time AI convos?. In 5 mins, we break down how Delphi + @MaterializeInc tackle….

How do you evolve your application architecture from human-scale to agent-scale? Here's how @withdelphi uses @MaterializeInc to do it.

Differential Dataflow, when combined with SQL via @MaterializeInc, gives engineering and data teams the best of both worlds: extremely performant incremental view maintenance with an accessible interface.

Agents generate more data and place more demand on systems than ever before—and standards like MCP will only accelerate this trend. Learn how @withdelphi is rethinking how they build data-intensive applications—from the db to the UI:

Checkout the release blog here!

Wether to keep up with the demands of AI projects or provide more dynamic customer experiences, your teams need to transform, deliver, and act on fast changing data no matter where it resides. Today, we’re making that possible by moving Self-Managed @MaterializeInc out of beta

Great first day at #GartnerDA with the @MaterializeInc team! Lots of interest in using MZ to build trustworthy real-time data products to support AI initiatives

.@MaterializeInc is now available to run fully managed, or self-managed in public or private clouds. Check out our early access program and some of the technical decisions behind our approach!

. @MaterializeInc might be the fastest way to add a "speed layer" to a lambda architecture.

RT @JLDLaughlin: it’s iconic and it’s accurate 💅. if you’re at Coalesce this week, drop by the @MaterializeInc booth to learn how you can d….

You can learn more about this pattern here:

With @MaterializeInc , you can subscribe to real-time changes that would invalidate a specific cache entry. Even more surprisingly, because you’re using an incrementally updated view, the work required to detect an invalid cache entry is the same work needed to get the updated.

It turns out that when you combine incremental view maintenance (IVM) with the ability to subscribe to real-time changes—not just database updates, but updates that affect the result of a specific query—you can solve three of the biggest problems with caches:. 1. How do you.

. so they can get out of sync and cause subtle bugs. More problematically, the work required to query the original database to get the cache back in sync can place excessive strain on the database, potentially destabilizing it.

Anyone using Facebook in 2010 *may* remember the cache stampede that caused their databases to fail and took their whole service offline. Anyone working at Facebook then *definitely* remembers. Databases keep businesses online, and caches keep databases online. The problem is,.

Heading to @confluentinc's #Current24 with the @MaterializeInc team! If you are attending I'd love to share what we're doing in the operational data space and how it can support your architecture and team goals.

For teams building services, it's common knowledge that if you want the freshest data possible—and you need it to be perfectly correct—you directly query the transactional database that logs what's happening *right now*. Turns out with access to incremental view maintenance you.