Radoslaw Szulgo
Senior Product Manager, Percona
Gdańsk, Poland
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Radoslaw is a Senior Product Manager at Percona, responsible for driving Percona Software for MongoDB. He has over 15 years of experience in building distributed applications, serving as a product manager, leader, and software engineer. Radek is an Agile product development enthusiast and a fan of personal development. Outside of work, he's an amateur volleyball player.
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Area of Expertise
Seamless MongoDB Migrations: Continuous Cluster Syncing in a Cloud-Native World
Many database deployments remain legacy, running on virtual or even bare-metal infrastructure, because we're afraid of a scary migration. But it doesn’t have to mean downtime or complexity. In this session, we’ll showcase Percona ClusterSync for MongoDB, a new open source solution for asynchronous, continuous cluster-to-cluster replication. Learn how ClusterSync and the Percona Operator for MongoDB make data mobility simple—enabling hybrid deployments, zero-downtime migrations, and cloud-native MongoDB operations with confidence.
MongoDB on the Move: Zero-Downtime Migration with Percona ClusterSync
Moving MongoDB to Kubernetes used to mean downtime and manual data juggling. Not anymore. In this lightning talk, discover how Percona ClusterSync for MongoDB enables continuous, asynchronous cluster-to-cluster replication—making zero-downtime migrations and hybrid deployments possible with just a few commands. Discover how ClusterSync and Percona Operator for MongoDB enable data mobility that is cloud-native, open source, and fast.
Vector Search on Your Terms: From Prototype to Production with Percona for MongoDB
Vector Search is now freely available in MongoDB Community Edition - but "available" and "production-ready" are not the same thing. This session, the first public deep-dive into Percona Search for MongoDB, walks through what it actually takes to run vector search at enterprise scale on self-managed infrastructure.
We open where every Percona Server for MongoDB user eventually arrives: your application needs to find documents by meaning, not just by exact field values. We build the mental model from scratch - full-text search for the keyword gap, vector search for the semantic gap, hybrid search that combines both, and RAG that closes the loop to natural-language Q&A - all grounded in MongoDB data patterns your team already works with.
Then we get honest about the gaps, as running vector search in production on self-managed MongoDB means solving two layers of problems at once:
1. The embedding pipeline: choosing among 100+ models, integration, and eventually building MLOps queuing infrastructure just to ingest at scale.
2. Enterprise readiness: upstream Vector Search is still in Public Preview, with no production-grade authentication integration, no observability into service and index health, and no automated deployment story for Day 1 and Day 2 operations.
We'll walk through a real reference deployment showing how to address these challenges in practice: from existing database deployment to extending it with Vector Search capabilities powered by automatic embeddings, enterprise authentication, and PMM monitoring, covering your vector indexes alongside your data.
The session closes with a live demo of the complete Percona Search for MongoDB stack with a demo application.
Radoslaw Szulgo
Senior Product Manager, Percona
Gdańsk, Poland
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