SurrealDB Raises $23M to Power AI-Driven Applications

We’ve reached a point where databases are scaling not just for users, but for intelligent systems working alongside us. As AI agents start generating their own schemas, queries, and workloads, the definition of “scale” is changing fast, from managing data volume and concurrency to handling millions of dynamic tables and unpredictable SQL. TiDB’s distributed SQL architecture and schema scalability naturally evolved to support that future, making it a strong fit for the AIaaS world that’s taking shape. Curious to see how this shift is unfolding? Dive into the full article here: https://ow.ly/2FiL30sUxil

We’ve reached a point where databases are scaling not just for users, but for intelligent systems working alongside us. As AI agents start generating their own schemas, queries, and workloads, the definition of “scale” is changing fast, from managing data volume and concurrency to handling millions of dynamic tables and unpredictable SQL. TiDB’s distributed SQL architecture and schema scalability naturally evolved to support that future, making it a strong fit for the AIaaS world that’s taking shape. Curious to see how this shift is unfolding? Dive into the full article here: https://ow.ly/9JUB30sUxim

MariaDB is acquiring GridGain — the pioneer of in-memory computing and creator of open source Apache Ignite. AI is only as powerful as it is fast. And we're closing the "AI latency gap." Most enterprises today are running AI on infrastructure that wasn't built for it. In business, a three-second delay is the difference between a completed sale and a lost customer. By merging MariaDB's AI-ready database with GridGain's lightning-fast in-memory power, we're setting a new industry standard — moving businesses from Slow AI to Instant AI. What does this mean for you? ✅ Faster decisions — act on data before the moment passes ✅ Seamless customer experiences — zero lag, zero friction ✅ AI that doesn't just think — it acts, in real time The future of business belongs to the fast. We're here to make sure you're leading the pack. [Read the full announcement] https://lnkd.in/g-huPsEM

If AI is the headline, metadata, governance, and open interoperability are the first few chapters. As data literacy improves and business teams adopt AI-assisted analytics and natural-language querying, the lack of discoverability will increasingly limit scalable self-serve analytics. That is the problem this Polaris and Iceberg proof of concept is designed to address. For our data organization, a unified catalog and open table foundation are foundational to making data easier to find, trust, and query across tools. Kudos to Julio Pereira Vilela and our Data Engineering team for driving this exploration and documenting the journey with clarity and rigor. Excited for what comes next.

One of the biggest blockers in modern data initiatives isn’t the platform, it’s the migration. Slow, risky, and unpredictable! This latest update from Databricks is a step in the right direction: https://lnkd.in/eJRtiU9v They’re leaning into AI-assisted migrations: ✔ Automated SQL conversion ✔ Early risk detection ✔ More predictable timelines The shift is clear: migration is becoming a key enabler for AI readiness, not just a technical task. At ProvenBI, we’re seeing this firsthand. Teams aren’t asking if they should modernize, they’re asking how fast they can do it without disruption. #Databricks #DataMigration #AI #ModernDataPlatform #DataEngineering #ProvenBI

AI agents are no longer just writing code. They’re building the data layer itself. New data shows agents now create 80% of enterprise databases and 97% of dev and test environments. This is evidence of a broader shift in data infrastructure - it needs to evolve to support building intelligent apps at machine-speed iteration. Nikita Shamgunov, VP at Databricks and co-founder of Neon, explains what is driving the move to a new kind of database: “The database is the system of record for AI applications. It’s no longer just a place to store rows; it’s the persistent memory and coordination layer for multi-agent systems.” More from Forbes:

Real-time isn’t a feature anymore. It’s the baseline. In most conversations I’m having right now, the challenge isn’t access to data… it’s how quickly teams can act on it. Batch pipelines were fine when latency didn’t matter. Today, they create blind spots across analytics, operations, and AI. That’s where Change Data Capture (CDC) comes in. With PostgreSQL, CDC leverages WAL to stream changes in real time, giving teams a way to move from delayed reporting to immediate action. I put together a practical guide on how this works and what it actually looks like to implement: 👉 https://lnkd.in/ge9f2FNN Curious how others are thinking about this. Are you still optimizing batch… or fully leaning into real-time?

Agents have changed how our customers think about running their business — especially when it comes to building new apps. It’s wild that the database layer… mostly hasn’t been impacted by AI yet. Take a minute to read about Lakebase. It feels like a real step-change: an AI-first database model that modernizes the foundation.

Enterprise data teams moving Agentic AI into production are hitting a consistent failure point at the data tier. Agents built across a vector store, a relational database, a graph store and a lakehouse require sync pipelines to keep context current. Under production load, that context goes stale ! https://lnkd.in/giyPqdJC #aidata #aiagents

If you haven’t already, check out Arun Ulag’s hero blog “FabCon and SQLCon 2026: Unifying databases and Fabric on a single, complete platform” for a complete look at all of our FabCon and SQLCon announcements across both Fabric and our database offerings.  New announcements from FabCon | SQLCon As organizations accelerate their AI adoption, the … Continue reading “Advancing Databases for the Next Generation of Applications” [https://lnkd.in/gs4wd7SS] #MicrosoftFabric #MSFTAdvocate