Abstract

Bio

Piotr Migdal is a founding engineer and AI specialist with a strong background in data visualization, machine learning, and applied research. He is a Founding Engineer at Quesma, where he leads development of Quesma Charts, using AI to transform data from sources such as CSV, SQL, and APIs into accurate, high quality visualizations for tools including ggplot2 and Grafana. Previously, he worked as an independent AI consultant, delivering user facing AI and data science solutions across medtech, biotech, gaming, and emerging technologies. Piotr is also the co founder and former CTO of Quantum Flytrap, focused on intuitive interfaces for quantum computing, and has experience as an AI researcher in game development and physics based simulation. He combines deep technical expertise with a strong emphasis on usability and clear communication of complex data.

Abstract

Every Node process you deploy runs on an operating system built for thousands of programs it will never run. Drivers for hardware you don't have. A scheduler managing processes that don't exist. You pay for all of it in image size, boot time, and attack surface. Unikernels compile the application together with only the OS parts it actually uses, producing a single specialized VM image. One app, one kernel, nothing underneath. I'll take an ordinary Express server and boot it as its own operating system, live. Then I'll spend the second half on what breaks. Some of it fails outright. Some of it works in ways that will surprise you once you know there's no separate userspace. By the end you'll know what a JavaScript runtime actually requires from a machine, and how much of the stack below your code is optional.

Bio

Utibeabasi Umanah is an SRE at Unikraft. Before that he built and operated distributed systems at scale, from multi-region IPFS gateways at Fleek to production Kubernetes platforms at Matrica and infrastructure at HeartStamp. He spends most of his time thinking about isolation, sandboxing, and making infrastructure disappear behind good abstractions.

Abstract

Databases for logs are usually forced to pick a side: build heavy inverted indexes for the ingested logs and pay for them on every write, or ingest raw data cheaply and pay with slow queries later. Is there a better approach? Yes - to make brute-force scanning so selective that most data is never read. This approach is taken by VictoriaLogs. This talk follows a log entry through the VictoriaLogs engine: how it is ingested with no upfront schema, how it lands in an immutable LSM-like compressed column-oriented storage, and how queries run the same structure in reverse, applying a stack of pruning mechanisms based on time, stream labels, and bloom filters so that only the data that can actually match is ever read. Beyond one system's internals, this is a talk about a design stance: when you specialize in one kind of data and know exactly how it is laid out and how it will be consumed, you can be smarter about what you build, sometimes discarding indexes entirely, and avoiding reading most of the data at all. You leave with a concrete set of patterns you can apply to your own storage problems, whether the data is logs or something else entirely.

Bio

Aliaksandr is a co-founder and the principal architect of VictoriaMetrics. He is also a well-known author of the popular performance-oriented libraries: fasthttp, fastcache and quicktemplate. Prior to VictoriaMetrics, Aliaksandr held CTO and Architect roles with adtech companies serving high volumes of traffic. He holds a Master’s Degree in Computer Software Engineering. He decided to found VictoriaMetrics after experiencing the shortcomings of all available time series databases and observability solutions.