Materialize

Operational data changes continuously. Iceberg was designed for batch commits. Materialize’s new Iceberg sink bridges that gap by delivering transactionally consistent operational data into Iceberg without the memory and latency costs of batching. Under the hood: logical timestamps, delete semantics, recovery without external state, and the open challenge of multi-table consistency. If Kappa means compute once and serve everywhere, this is how. Read more → bit.ly/40pNwqh

Agents don’t fail in production because models are bad. They fail because context is stale, fragmented, or too slow. See how @Day_ai_app built an agentic CRM, with live context powered by Materialize 🔗bit.ly/4sJLsGu

Flare's microservices architecture impacted client experience and held back product development. With Materialize + dbt, they built a live data layer across MongoDB, Salesforce, and more—powering sub-second queries and enabling a unified case view, a reliable “My Clients” dashboard, and fast feature computation for AI matching. Full story: bit.ly/4oaVdtE

Introducing new Materialize Cloud M.1 Clusters — bigger workloads, better economics, same Materialize. 🚀 3x larger workloads ⚡️ <1s p99 latency 🏎️ Single-digit millisecond query response times Bigger scale. Better value. Same freshness, responsiveness, and correctness. bit.ly/43zz7cL

Materialize is heading to the Gartner IT Symposium/Xpo™ next week. Visit us at Booth #224 to learn how Materialize brings real-time data streaming and analytics to life - transforming how teams build intelligent, responsive applications.

Operational data products are reshaping how apps & AI consume data. But should you bet on Materialize or Palantir Foundry 📄 Download the side-by-side comparison: bit.ly/4ol1CTN

@MaterializeInc offers a simpler solution. Instead of reactively scanning millions of rows when updates happen, Materialize proactively and correctly maintains live representations of your core business entities as views, shifting computation from query time to write time with ~10ms access to fresh derived data. Push changes as they occur to your vector store.

We’re excited to share that Materialize is hosting the next NYC Institute for Data, Engineering, Architecture, and Standards (IDEAS) Meetup in partnership with @Snowflake. Join us on September 25th at 5:30 PM ET hubs.la/Q03KkwBF0

Vector DBs are useless with stale context.  Materialize keeps attributes fresh with incremental updates—no more costly re-computes, no fragile pipelines. ⚡️ Fresh vectors, simpler stacks. bit.ly/3IamUUJ

Welcome Frank McSherry @frankmcsherry to Sync Conf 2025. Pioneer of sync technology, inventor of Differential Dataflow, and founder of @MaterializeInc, Frank will trace the evolution of sync and stream processing.

🚀 Big release: Materialize now uses swap to scale SQL workloads beyond RAM. The results: 🏎️ Faster hydration 🚀 Efficient memory utilization 🗂 Larger workloads support Read @antiguru_de's deep dive → bit.ly/4grbGro

🗣 Shoutout to our #OpenSearchCon silver sponsor @materializeinc. Join us September 8-10 in San Jose, CA to connect with #OpenSearch users, admin, & developers exploring the future of search. Register: hubs.la/Q03Gx8VY0 Schedule: hubs.la/Q03Gx8d00

Live today at 2 PM ET: [Webinar] Transform SQL Views into Real-Time AI Agent Tools See how Materialize turns SQL views into callable APIs with strong consistency + sub-second freshness. Register here: hubs.la/Q03FRByS0

What happens when Materialize R&D gets a day and a half to build whatever they want? 🔹WASM UDFs 🔹 S3-backed upsert 🔹 Formal verification 🔹 EXPLAIN ANALYZE (now live!) All from our hackathon 🔗 bit.ly/4oGdXTu  #streamingSQL #databases #rustlang

Real-time or historical? Our new guide breaks down how Materialize and ClickHouse solve different data problems — and why you might need both. Read the full comparison → bit.ly/3Hxt6Wf #DataEngineering #ClickHouse #Materialize #StreamingSQL #AIInfra

Waiting for CI hurts. In July, we cut our runtime by up to 86%. From 23+ min builds to under 2 min, and full runs in as little as 7 min. Caching, parallelization, smarter builds, and a bit of [libeatmydata] magic. How we did it🔗 bit.ly/4me14OP

AI agents fail without live context. A digital twin gives them a real-time, queryable model of your business — built with Materialize & SQL. Here’s how to make your AI context-aware 🔗 bit.ly/4opA4xm #AI #realtimedata #digitaltwin

Materialize skips irrelevant data before reading it.  It’s called filter pushdown, and it cuts object store traffic by 50%+ using stats + static analysis.  Faster queries, lower cost. 🔗 bit.ly/4miILr4

Tap into Bluesky’s public firehose, land it in @MaterializeInc, and ask SQL questions on live social data—all in a few lines of code. Walkthrough by @frankmcsherry → bit.ly/3IwGWIJ  #StreamingSQL #Bluesky

SELECT without limits: Materialize now streams big results out-of-band, freeing the control plane and slashing memory pressure—delivering faster dashboards. Details → bit.ly/4ePXzLp