Nate Stewart

How to create a live data layer for agents without blowing up your production databases.

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: bit.ly/44i2hg2

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…

How do you scale to handle thousands of real-time AI convos? In 5 mins, we break down how Delphi + @MaterializeInc tackled the infra challenge of querying agent-generated data: fast, reliably, and at scale. Watch 👀👇

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: bit.ly/42BZdf9

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! bit.ly/3VVa2W1

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

it’s iconic and it’s accurate 💅 if you’re at Coalesce this week, drop by the @MaterializeInc booth to learn how you can dbt run your models once… and never again