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.
2026-06-10
1,161 reads
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.
2026-05-20
752 reads
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.
2026-05-06
1,280 reads
This stairway addresses many of the issues that come about with ad hoc database deployment processes in many organizations. You will learn how to better architect and structure your processes to ensure a reliable database deployment for your applications.
2026-04-15
428 reads
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.
2026-04-15
2,014 reads
Today Steve talks about the concept of what a failure is when deploying changes.
2025-06-13
93 reads
This guide will walk you through the process of deploy DACPAC to Azure SQL database directly from Visual Studio.
2025-03-10
1,871 reads
If you build software for customers, you have a pipeline. It might not be good, but you have one. Steve recommends you work to make sure this is a repeatable, reliable pipeline.
2023-10-13
172 reads
You’ve got an existing application with a database back end. You’re thinking about changing the database, and you don’t wanna break stuff.
2023-07-28
You want to use SQL Compare or SQL Change Automation (SCA) to create or update a database, and at the same time ensure that its data is as you expect. You want to avoid running any additional PowerShell scripting every time you do it, and you want to keep everything in source control, including the data. You just want to keep everything simple. Phil Factor demonstrates how it's done, by generating MERGE scripts from a stored procedure.
2019-12-13
By James Serra
Making Data AI-Ready, Part 3 (This is the final article in a three-part series...
By Steve Jones
I use ConEmu for my terminal interface. I’m still on Windows 10 at home,...
Creating a Fabric workspace takes about 30 seconds. Restructuring workspaces after people have built...
WhatsApp Cs 628218520153 Gedung Grand Indonesia Mall A, Lower Ground No. 20-21, Jl. M.H....
WhatsApp Cs 628218520153 Pusat Grosir Senen Jaya Blok A8-9, Lantai 2, RW.3, Senen, Kec....
WhatsApp Cs 628218520153 Jl. P Jayakarta Komp. No.127, RT.7/RW.7, Mangga Dua Sel., Kecamatan Sawah...
Which number did the two COUNT(*) return:
DROP TABLE IF EXISTS #tmp CREATE TABLE #tmp (id INT NOT NULL) INSERT INTO #tmp (id) SELECT gs.value FROM GENERATE_SERIES(1, 5) AS gs ALTER TABLE #tmp ADD my_value INT NOT NULL CONSTRAINT df_tmp_my_value DEFAULT 1 SELECT COUNT(*) FROM #tmp AS t WHERE my_value = 1 ALTER TABLE #tmp DROP CONSTRAINT df_tmp_my_value ALTER TABLE #tmp ADD CONSTRAINT df_tmp_my_value DEFAULT 2 FOR my_value SELECT COUNT(*) FROM #tmp AS t WHERE my_value = 1See possible answers