When working with embeddings in PostgreSQL, particularly for use cases like semantic search, recommendation systems, or retrieval-augmented generation (RAG), how you prepare and ingest data matters just as much as how you query it.

At first glance, storing and querying embeddings in PostgreSQL may seem impractical—but with the right setup, it’s both efficient and effective. This post covers how to design your schema, store vectors properly, and perform fast similarity searches without the usual headaches.

Embeddings are the foundation of vector search, allowing us to represent meaning-rich content like documents or queries as numerical vectors. But to use them effectively, it’s essential to understand what’s actually being embedded—whether that’s individual words, full sentences, or larger chunks of text.

As vector search becomes a foundational feature in modern applications—from semantic search and recommendation engines to AI-driven insights—developers are increasingly adopting PostgreSQL with the pgvector extension. However, one concept often creates confusion: the difference betweensimilarity anddistance.

For many organizations, the race to adopt AI is already underway. But too often, projects stall, not because of the models, but because the data underneath them is chaotic, inconsistent, or poorly understood.

A recent cryptomining attack on PostgreSQL servers revealed how misconfigurations and weak credentials can be exploited to deploy fileless malware, underscoring the need for proactive, layered security measures to defend against evolving threats. Nishchay Kothari explains.

It was an exciting experience to share the stage with my Fujitsu coleague Nishchay Kothari PGConf.dev 2025 to present our session on the topic ofLearned Indexes in PostgreSQL. For those that could not attend, we would like to share our presentation.

In today’s cloud-native world, security isn’t just a checkbox — it’s foundational. When managing business-critical workloads like databases in Kubernetes, particularly with robust platforms such asFujitsu Enterprise Postgres, ensuring secure access to sensitive information like credentials and TLS certificates is a must. This is where HashiCorp Vault comes into play.

Fujitsu has releasedFujitsu Enterprise Postgres 17 SP1, with the aim of providing a secure and user-friendly data infrastructure for AI applications. There are a lot of exciting capabilities added, let me take your through them.

Fujitsu has participated in the PostgreSQL community for over 20 years and has contributed to PostgreSQL development by developing new features, as well as creating and reviewing correction patches. And last year we took our team contributions to a whole new level.