Materialize
@MaterializeInc
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The live data layer for apps and agents
New York, NY
Joined April 2019
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 🔗 #streamingSQL #databases #rustlang
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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 → #DataEngineering #ClickHouse #Materialize #StreamingSQL #AIInfra
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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🔗
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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 🔗 #AI #realtimedata #digitaltwin
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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. 🔗
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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 → #StreamingSQL #Bluesky
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SELECT without limits: Materialize now streams big results out-of-band, freeing the control plane and slashing memory pressure—delivering faster dashboards. Details →
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RT @Nate_Stewart: How to create a live data layer for agents without blowing up your production databases.
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Evaluating Flink vs. Materialize?. This guide breaks down key differences in architecture, cost, developer experience, and consistency. A must-read for enterprise teams working with real-time data. đź”—
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AI agents don’t just ask to chat—they act. And when your data isn’t fresh and trustworthy, their decisions can cascade into chaos. To stay grounded in reality, agents need digital twins: governed, semantic views of business state that update in real-time.
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🚀 Materialize 25.2 is here! New features include live freshness reports for all your views, 2.5x faster data product deployment times, and native SQL Server support. See how these updates can help streamline your operations:
materialize.com
Materialize v25.2 introduces exciting new features like columnarized persistent storage for 80% smaller storage size, improved Kafka sink rehydration times, and enhanced security with role-based...
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:
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RT @withdelphi: Delphi has raised a $16M Series A, led by @sequoia with participation from @MenloVentures's Anthology Fund, @crossbeamvc, a….
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Delphi is turning expert knowledge into real-time, conversational systems. They needed fast, flexible, always-fresh data infrastructure. Materialize is proud to power part of the stack. $16M Series A led by Sequoia ➡️👇.
All the minds in the world, now one question away. Watch what we’re building at @withdelphi.👇🏼.
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📢 Materialize now supports native SQL Server. ✅ No Kafka.✅ No Debezium.✅ No polling.✅ Just always-fresh, always-correct views. Define your logic once. We keep it live. This is what operational data products are made of. 🔗
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Our engineering team at Materialize recently uncovered a rare concurrency bug 🪲in Rust’s 🦀 unbounded channels that could lead to double-free memory errors. After thorough debugging and collaboration with the Rust and crossbeam communities, the fix is now part of @rustlang.
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RT @Nate_Stewart: Enterprise data infra is breaking under the weight of AI agents. MCP gives agents access to services—including databases….
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