Adrien Obernesser

AI problems are database problems

Introduction When discussing agentic AI, we spend a lot of time on the model. Which one should we use? How much context does it support? Can it choose the right tool? These are useful questions. But from a my(…)

AI agents on sensitive data: what PostgreSQL can enforce, and what it can’t

Every useful database content, document behind an internal AI agent names someone, a company, server names,… A relationship manager might ask “what did this client complain about last quarter?”, an operations engineer might ask “what happened on this server(…)

Surviving a Patroni failover with logical replication

Your Patroni cluster does exactly what you built it to do. A node dies at 3 a.m., a new primary is elected in a few seconds, the application reconnects, and nobody gets paged. Your HA setup works. And(…)

MariaDB 12.3 – Binlog Inside InnoDB

MariaDB has been quite active lately, the version 11.8 was already quite a step forward and apart from the changes on LTS schedule and the EOL durations, the RC 12.3 LTS is bringing some interesting changes to binlogs, pushing(…)

pgvector, a guide for DBA – Part 2: Indexes (update march 2026)

Introduction In Part 1 of this series, we covered what pgvector is, how embeddings work, and how to store them in PostgreSQL. We ended with a working similarity search — but on a sequential scan. That works fine(…)

RAG Series – Embedding Versioning LAB

Introduction This is Part 2 of the embedding versionin, in Part 1, I covered the theory: why event-driven embedding refresh matters, the three levels of architecture (triggers, logical replication, Flink CDC), and how to detect(…)

RAG Series – Embedding Versioning with pgvector: Why Event-Driven Architecture Is a Precondition to AI data workflows

Introduction “Make it simple.” This is a principle I keep repeating, and I’ll repeat it again here. Because when it comes to keeping your RAG system’s embeddings fresh, the industry has somehow made it complicated. External orchestrators, custom Python(…)

RAG, MCP, Skills — Three Paradigms for LLMs Talking to Your Database, and Why Governance Changes Everything

Introduction Throughout my RAG series, I’ve explored how to build retrieval-augmented generation systems with PostgreSQL and pgvector — from Naive RAG through Hybrid Search, Adaptive RAG, Agentic RAG, and Embedding Versioning (not yet(…)

Oracle to PostgreSQL Migration with Flink CDC

Introduction When wanting to migrate from the big red to PostgreSQL, most of the time you can afford the downtime of the export/import process and starting from something fresh. It is simple and reliable. Ora2pg being one of the(…)

RAG Series – Agentic RAG

Introduction In earlier parts, we moved from Naive RAG (vector search) to Hybrid RAG (dense + sparse) to Adaptive RAG (query classification and dynamic weighting). Each step improved what we retrieve. Agentic RAG goes further: the LLM decides if(…)