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Iceberg and the future of Streaming Data Architectures Alex from Dremio, will be joining our host Hugh Evans. Alex is making data lakes more accessible in his role as a developer advocate at Dremio. What we’ll cover.

Ready to master anomaly detection? Join us for Part 2 of our "Applying AI/ML in Observability" series, where we do a deep dive into vmanomaly! In this live stream, Mathis and Marc will be joined by a very special guest: Fred Navruzov, the lead developer and mastermind behind VictoriaMetrics' vmanomaly. If you want to move beyond the basics and unlock the full potential of AI-driven observability, this is a session you can't afford to miss.

Storage is often underestimated: In infrastructure discussions, compute and networking get most of the attention, while storage is treated as secondary. For AI workloads, that can be a costly oversight. Data throughput for specialized hardware: AI infrastructure powered by GPUs can process massive volumes of data at unprecedented speeds. This puts immense pressure on the storage system to keep up. Scale-out performance: An on-prem, scale-out, software-defined storage setup allows you to meet high performance demands, grow capacity as needed, and stay in control of infrastructure costs.

Modular architecture: Decouple compute from storage so each can scale independently. This makes it easier to adapt to growing or shifting workloads over time. Future-ready hardware: Select GPUs and CPUs not just for current workloads but with an eye on scalability, including support for newer accelerator types. Scalable design: Ensure the system allows seamless addition of compute nodes or storage without a full redesign.