# From stale data to real-time digital twins | April News Canonical: https://brew.new/browse/templates/email/pt1_k97vfg0t6a8yhbtja6kj9yqzr58e4bp9 Brand: materialize.com Category: newsletter ![Preview of From stale data to real-time digital twins | April News](https://cdn.brew.new/email-preview-bc9bb87c762f1308-tracking_r57vmt65599mehm8w96dkaepd98dv9m0-1789009170774.png) ## Email content View in browser Materialize NEWSLETTER APRIL 2026 Real-time digital twins for the supply chain At Materialize, we spend a lot of time talking with engineering teams, and they're almost always wrestling with the same core problems. Data is scattered across databases, messaging queues, and third-party platforms, so they rarely have a complete view of what's happening. Processes are batch-based, so by the time they have the data they need, it's already stale. And they're spending time massaging data and wiring systems together, instead of building features that actually matter to customers. We see this across industries, from financial services and gaming to legal and edtech. But lately, we're seeing this more in manufacturing, distribution, and logistics in particular. Data is scattered across ERPs, warehouse platforms, and transportation management systems, and so the biggest challenge is in bringing it together, and making it live. Engineering teams are turning to Materialize to bridge that gap, to turn those data silos into real-time views and data products. For example, one company we work with went from week-old inventory snapshots across 8 databases to live views that reflect what's in stock right now. Another cut shipment notification latency from 30 minutes to under 10 seconds. A third started with live freight tracking across 2,600+ trucks but now has four use cases in production, each built on the one before it. These companies now have a real-time digital twin of their operations, and with it, the foundation to power their apps and agents. It's a space we're excited about. We'll be at MODEX in Atlanta April 13–16, and we're looking forward to discussing this and more with engineering leaders there. If any of this resonates, we'd love to chat more. -The Materialize Team 🚀 What's New in Materialize This month's releases focus on getting data in faster, speeding up deploys, and adding source versioning for SQL Server. S3 and bulk data loading. You can now load CSV and Parquet files directly from Amazon S3. COPY FROM supports both formats from any S3-compatible storage, including Google Cloud Storage, Cloudflare R2, and MinIO. Wherever your data lives, it's now even faster and easier to get it into Materialize. Faster catalog operations and deploys. Creating views, indexes, and dropping objects is now 37-55% faster, even in environments with hundreds or thousands of objects. Batching multiple operations in a single transaction brings further gains of up to 10%. Blue/green deploys via dbt-materialize now batch readiness checks instead of polling clusters one by one, so you spend less time waiting on deploys and more time building. SQL Server source versioning. Source versioning is now available for SQL Server in private preview, so you can handle upstream schema changes without recreating your sources. Read the guide for details, or contact our team to enable it in your environment. We've also shipped several performance and efficiency improvements, including up to 25% lower memory for joins on varchar and text columns, incremental WebSocket streaming, and faster Iceberg sink commits. For the full list of changes, visit the Materialize Release Notes. 📖 Latest from the Materialize Blog Speeding Up Timely Dataflow by 100x A deep dive into how a simple change unlocked massive performance gains - and why Timely Dataflow’s approach to progress tracking is fundamentally more efficient. The New Agentic Data Architecture: A Live Operational Data Mesh Why AI agents need more than context engineering - and how a live data layer eliminates stale data, latency, and coordination issues in production systems. Why You’re Doing Context Engineering Wrong Most teams focus on prompts - but the real problem is data. This post breaks down why live data architecture is the missing piece for reliable AI systems. How AI Agents Are Redefining Digital Twins Digital twins are evolving from static models to live systems - powered by data that keeps AI agents aligned with reality. No Classification Without Representation A thought-provoking look at why better data representation is foundational to building accurate, reliable AI systems. 📅 Upcoming Events MODEX 2026 Atlanta, GA | April 12-16th, 2026 Connect with Us > AI Agent Conference New York, NY | May 4 - 5th, 2026 Confluent Data Stream World Tour Chicago, IL | May 14th, 2026 In case you missed our last webinar, view the recording for “Real-World Context Engineering: AI Agents in the Enterprise” Recording > Check out our upcoming events and see where the Materialize team will be next. Materialize slack-1 LinkedIn X social-icons Materialize, Inc, 436 Lafayette Street, Floor 6, New York, NY 10003, United States Unsubscribe Manage preferences [Open and remix this design](https://brew.new/browse/templates/email/pt1_k97vfg0t6a8yhbtja6kj9yqzr58e4bp9) [Browse email designs](https://brew.new/browse/templates)