Operations | Monitoring | ITSM | DevOps | Cloud

Like many of you, over the last couple of years, I’ve been using AI, or, well, let’s just name it appropriately, Large Language Models (LLM), as a part of my job. I’ve also used it in my hobby. With it, I’ve generated snippets of code, tested data conversions, even built a small database for a presentation. However, to date, I haven’t tried doing everything through the LLM. Now, I’m going to.

Redgate Monitor now surfaces two common SQL Server query issues that usually take manual work to uncover: cancelled or aborted queries and high memory-grant queries. You can now see both in the Query Executions view for each SQL Server instance, directly alongside server activity and alerts, so you can diagnose the cause much faster. Recently, Redgate Monitor introduced the Query Executions feature for SQL Server instances, using Extended Events to capture execution details for individual queries.

Redgate Test Data Manager’s November’s release brings advanced table configuration for large databases and the ability to save connection strings. Two features that make setting up subsets and managing database connections smoother, easier, and faster.

With increasing security threats and stringent compliance requirements, database code quality isn’t just a best practice; it’s a business imperative. Yet many organizations struggle to enforce their database development standards consistently across teams, leading to security vulnerabilities, potential data loss, and lengthy review cycles that slow down software delivery.

There’s growing unease in the database world regarding delivering at speed, raising the question – just how do we keep up with the pace of change without losing control of the things that matter most? AI is rapidly transforming the mechanics of how code is written, reviewed, and optimized which in-turn, increases the risk of destabilization.

The latest release of Redgate Monitor visualizes Oracle wait classes and events across single-instance, multitenant (CDB/PDB), and Data Guard environments. It gives DBAs a clear, high-level view of where database time is spent and makes diagnosing and resolving performance issues in Oracle much simpler.

Learn how Redgate’s Foundry drives AI innovation in database management - from intelligent monitoring and ML-based automation, to smarter SQL optimization. In today’s rapidly evolving database landscape, innovation is essential. With the rise of artificial intelligence (AI), machine learning (ML), and automation, database management is undergoing one of its most significant transformations in decades.

Flyway provides a lot of flexibility for releasing database changes in a safe and repeatable manner. Earlier this year, we added the ability to automate state-based deployments. This means the structure of the database is defined in version control and Flyway handles updating a target database to match it. The Flyway comparison engine does all the hard work of identifying what’s different and creating a script that will run on the target database to alter it so it matches the latest state.

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.

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.