Have you ever wondered what’s really going on under the hood of distributed systems? Not those “sort of a cluster” setups with 3 nodes, I mean the real deal. The exabyte-scale beasts. In this talk, we’ll peek behind the curtains of modern infrastructure. How do systems that crunch mountains of data actually work? What patterns, principles, and engineering decisions are hiding behind truly scalable architectures? Here’s what we’ll dive into: - What the inner life of a distributed system really looks like - How a distributed app is different from a distributed system - How data storage patterns evolve into modern DBs, queues, and logs - Why “PostgreSQL in the cloud” isn’t really PostgreSQL anymore - Why Northguard might just outshine Kafka - And how new players like NewSQL are changing the game If you’re an architect, tech lead, developer or just curious about why infrastructure scales the way it does - come join! I’ll share insights you might use in your own projects (or at least see them from a new angle). P.S. Yep, there’ll be a bit of magic ✨ and a whole lot of hard truths about the distributed systems powering our world. ?

JavaScript and Golang are two different worlds that often intersect in modern projects: the first dominates frontend development and rapid prototyping, while the second excels in high-load services and microservice architectures. In this talk, I will share my personal experience of moving from JS to Go, comparing approaches to asynchronous programming, application architecture, working with databases, and tooling. We’ll explore not only the differences but also the similarities that help developers adapt more easily between these ecosystems.

Do business approaches work in the defense sector? How can convenience and security be balanced? We’ll share our experience of building, developing, and implementing the DOT-Chain infrastructure from scratch.

The session covers architectural strategies that keep Elasticsearch stable under heavy load: proper index and shard design, ILM policies, persistent queues in Logstash, metrics downsampling, and self-monitoring of the observability stack. Insights are drawn from operating platforms that handle terabytes of logs and millions of events per day.

Design patterns like Event Sourcing and Event Streaming have long become standards for building real-time analytics systems. However, when the system load becomes nonlinear with fast and often unpredictable spikes, it's crucial to respond quickly in order not to lose real-time operating itself. In this talk, I’ll share my experience implementing and using a tool like Temporal.io. We'll explore the evolution of our system for maintaining real-time report generation and discuss how we use Temporal both for short-lived pipelines and long-running background tasks.

This talk is dedicated to the practical experience of transitioning from the classic Istio architecture with sidecar containers to a new model - Istio Ambient Mesh. We will tell how scaling a microservice infrastructure on Kubernetes led to increased resource costs, complicated CI/CD, and delays in launching services. The search for solutions led us to implement Ambient Mesh, an architecture without sidecar containers that greatly simplifies mesh integration and reduces infrastructure costs. The presentation will detail the technical aspects of the migration: how we prepared for the transition, what challenges we faced during the implementation process, and how we managed to overcome them in cooperation with the Istio community. We will share the results, analytics, real before/after metrics, and give practical advice to teams planning to implement Ambient Mesh in production.

When human safety is at stake, technical reliability is not just a requirement. In this talk, we'll look at architecture, workloads, WebSocket solutions, Kubernetes scaling, and other technical aspects of creating an airborne alarm map. This is a story not only about code, but also about responsibility.

Let's talk about our history. How we started the project with a small vector database of less than 2 million records. Later, we received a request for +100 million records, then another +100... And so gradually we reached almost 1 billion. Standard tools were quickly running out of steam - we were running into performance, index size, and very limited resources. After a long series of trials and errors, we built our own low-cost cluster, which today stably processes thousands of queries to more than 1B vectors.

Taking a system from 66 million to over 25 billion records is not for the faint of heart—especially when the system in question is financial, where precision is non-negotiable and data latency isn’t an option. In this talk, I’ll walk through the real-world journey of scaling such a system while staying sane and keeping the numbers right. You’ll get a look at how to juggle the need for ultra-high accuracy and low-latency performance, streamline your app logic, and sidestep the usual traps that come with database scaling. This session isn’t about deep-diving into internals—it’s about sharing pragmatic strategies that help you scale without drowning in complexity. Ideal for engineers and system architects eager to tackle serious scale challenges with clarity and confidence.</p> <h4>Abstract

We will explore how e-commerce projects prepare for the busiest time of the year, which key aspects to focus on, and what to expect. We’ll share our experience in setting up auto-scaling, load balancing, and discuss the loads that Silpo handles, as well as the solutions that help us navigate this season without failures.