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
The live data layer for agents and apps
- 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”
- 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.
- 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.

