Operations | Monitoring | ITSM | DevOps | Cloud

We are thrilled to announce a significant update across our DAC (Data Access Components) product line. This release focuses on expanding compatibility with the latest development environments, extending support for modern databases, strengthening security, and adding powerful new functionality for AI-driven applications.

Querying PostgreSQL in plain English is no longer a futuristic idea; it’s something you can build today. With a Model Context Protocol (MCP) server, Claude Desktop connects securely to external systems, including databases, through a structured interface. Here, MCP acts as the bridge between Claude and PostgreSQL, allowing it to run queries, explore schemas, and return results instantly in Markdown. This guide walks you through building that bridge in C#.

One kind of data in most relational databases is what we call static data. This is also referred to as lookup data, code data, domain data or even list data. Whatever you like to call it, it’s usually smaller data sets consisting of data that never changes, or changes very slowly. One example might be Canadian postal codes. Another example, and one I’m going to use, is the amateur radio band definitions within a given country.

Databases form the critical foundation of modern applications, and maintaining their performance and reliability is essential for operational efficiency and user satisfaction. Effective database monitoring however presents numerous challenges. Modern systems produce extensive metrics, operate across diverse environments, and must scale in line with growing workloads, all while ensuring compliance and security.

For nearly 20 years, SQL Prompt has been a trusted companion for database professionals, helping them write cleaner code, reduce errors, and save time. From its earliest days, the focus has been on productivity while removing the repetitive, mundane tasks that slow people down. It’s been about building the reliability and performance that developers and DBAs depend on every day and that story doesn’t stop here.

A few weeks ago, we massively improved the performance of the dashboard & website by optimizing some of our SQL queries. In this post, we'll share how we identified the queries that needed work. In the next post, we'll explore how we fixed each of them. We'll cover the basics and gradually work our way up to the more advanced/complex ways of identifying slow queries. In this post, you'll see: Let's go!

Until recently, Grafana Mimir — our open source, horizontally scalable, multi-tenant time series database (TSDB) — has exclusively used Prometheus’ PromQL engine to evaluate queries. While the PromQL engine works great, it sometimes needs a lot of memory to run, specifically in the Mimir querier component. To address this memory consumption issue, we recently introduced Mimir Query Engine (MQE).