PostgreSQL

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(…)

PostgreSQL Snapshot Backup and Restore with Proxmox ZFS (4/4)

In the blog series I previously wrote, I did not answer all the customer’s questions. The last one was the following: Can this also be applied to PostgreSQL? In short, yes, it is possible. Let’s see(…)

Upgrade RHEL from 9.6 to 10.1 (when running PostgreSQL/Patroni)

Upgrading from RHEL 9.6 to 10.1 is not just a routine update, it’s a major platform shift. When your server runs PostgreSQL compiled from source and a Patroni-managed cluster, the complexity increases significantly. System libraries change, Python environments break,(…)

Zabbix – Monitoring full cluster Patroni/PostgreSQL

Introduction Monitoring a PostgreSQL environment involves much more than simply checking whether the database is running. In a modern high availability architecture, multiple components work together to keep the service available and stable. In this article, we will explain how(…)

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(…)

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(…)

PostgreSQL Anonymizer: Simple Data Masking for DBAs

Sensitive data (names, emails, phone numbers, personal identifiers…) should not be freely exposed outside production. When you refresh a production database to a test or staging environment, or when analysts need access to real-looking data, anonymization becomes critical.

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(…)