Talks

How we solved the problem of making hotfix changes to ETL pipelines on Apache Spark in hundreds of existing processes without changing their code.

Our Trino storage hit the performance ceiling of a single Ceph cluster — so we started spreading every table across several clusters at once, hiding all the sharding logic in the HAProxy sidecars on our compute nodes, without adding a single new component to the architecture. Reads sped up from 20 to 60–80 GB/s, and GET latency dropped from minutes to 1–2 seconds.

Let's talk about testing, finding and debugging problems in highly loaded software, as well as support for storage with third-party vendor solutions.

In this talk, I will analyze a practical approach to measuring self-hosted LLM performance.

In the talk, I will review the current state of the data transformation ecosystem, as well as alternative tools and promising projects that may replace dbt.

Let's talk about what important functions are needed to manage Iceberg tables and the role of REST Catalog in this.

The talk is about practical experience in optimizing inference and ML-serving based on GPUStack in the production environment of the corporate AI Portal.

How pgvector works: vector storage, HNSW and IVFFlat algorithms, performance degradation points. An honest breakdown of where the solution holds up and where it doesn't.

The talk shows pitfalls that prevent the widespread use of sketches by final analysts.