Taking SQL art from shamrocks and Easter eggs to something your DBA manager might actually care about. If you've been following my SQL spatial art series — shamrocks, Easter eggs, Christmas trees — you'll know I have a habit of finding increasingly creative misuses for SQL Server's geometry data type. Most of them have been […]
Are computers getting smart enough to pass the Turing test or are humans getting worse at representing themselves as intelligent?
In this article, learn how PostgreSQL powers data science workflows, including query execution, performance optimization, indexing, data retrieval, and more.
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Stairway to Reliable Database Deployments introduces a progressive approach to managing database changes with clear intent, predictable rollback, and explicit behavioral guarantees. Starting from change design and moving toward execution and coordination in complex environments, the Stairway provides a conceptual framework for deploying database changes safely and consistently, independent of specific tools or automation platforms.
After defining the deployment contract in the previous level, this article focuses on validating a changeset before it reaches production. Rehearsal across environments ensures that execution order, rollback behavior, and baseline alignment all behave exactly as expected.
Level 2 formalizes the behavioral guarantees that a changeset must provide in order to be safely deployed and rolled back. It introduces the deployment contract, checkpoint semantics, and the structural scope of Create and Rollback scripts. Data changes are addressed through a dedicated Update mechanism, with clear boundaries and limitations. By the end of this level, a changeset becomes a predictable and well-defined unit that can be reasoned about independently of execution context.
This level examines how a rehearsed changeset is transformed into production-ready deployment artifacts. By consolidating scripts into controlled execution units and validating the resulting artifacts, the approach ensures that production deployment remains predictable and aligned with what was proven during rehearsal.
How do the experts get to that level? A few thoughts today in a guest editorial from Kathi Kellenberger.
You might notice that the advice you get from Copilot in SSMS isn’t very good, but it isn’t necessarily AI in general. Part of the problem is something specific about SSMS Copilot and the way it prompts LLMs, and I wish I could tell you exactly what it is, but I can’t because I don’t have access to how SSMS is changing your prompts.
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When running bcp on Linux, what is the field terminator?
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