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Dynamic PIVOT in SQL Server for Unknown Column Values

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Most pivot operations in SQL tend to use hardcoded column lists. This is where you explicitly specify every column name in the IN clause. That works great when you know exactly which values will appear in your data. But what happens when those values change? When new categories get added, when you’re working with user-generated data, or when you’re building a query that needs to work across different datasets?

That’s when you might want to consider dynamic PIVOT. Instead of hardcoding column names, you query the data to discover what columns you need, build the PIVOT query as a string, and execute it dynamically. It’s more complex than static PIVOT, but it’s pretty much essential when your column values aren’t predetermined.

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Calculating Days Between a Fixed Date and Dynamic Dates with DATEDIFF()

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Sometimes you need to measure how many days have passed between a specific reference point and a constantly moving target. SQL Server’s DATEDIFF() function handles this elegantly by letting you combine hardcoded dates with dynamic functions like GETDATE(). This can be useful for calculating things like age, days since an event, or time remaining until a deadline.

The main point here is that DATEDIFF() doesn’t care whether its date arguments are literals, functions, or even subqueries. It just needs two date values to compare. When you use GETDATE() or similar functions, you’re telling SQL Server to calculate the difference based on the current moment, which means the result changes every time you run the query.

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Restoring SQL Server Backups in Docker on macOS with VS Code

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Whether you are a long-time Mac user or recently transitioned from Windows, running SQL Server on macOS brings a unique set of challenges. Most tutorials assume you’re on Windows using SQL Server Management Studio (SSMS), which is a tool that doesn’t exist for Mac. Instead, you’re likely running SQL Server inside a Docker container and using a tool like VS Code.

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How to View Query Execution History Over Time in SQL Server

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In SQL Server, Query Store aggregates performance statistics into time intervals, allowing you to see how query performance changes over time. This historical view helps you identify when performance degraded, spot patterns like daily or hourly spikes, and correlate performance changes with deployments or data changes.

This assumes that Query Store is enabled on the database in question. So assuming Query Store is enabled on the database, you can use the following queries to check the execution history of queries over time.

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How to Install SQL Server on a Mac in 2026

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Running a full-featured Microsoft SQL Server on a Mac used to be a headache, but as of 2026, it is smoother than ever. Thanks to improvements in Docker Desktop and macOS’s Rosetta 2 translation, you can now run the enterprise-grade engine (including the new SQL Server 2025 Preview) directly on your M1, M2, M3, or M4 Mac.

This guide will walk you through setting up a modern SQL Server environment from scratch.

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Using DATEDIFF() with LEAD() to Calculate Time Until Future Events

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When you’re analyzing how events unfold over time in a SQL database, one of the biggest challenges can be efficiently comparing what’s happening now to what comes next. Each event typically appears as its own row with a timestamp, but meaningful insight often comes from understanding how those timestamps relate to one another. Fortunately SQL Server provides some useful tools for this kind of sequential analysis.

Rather than relying on bulky self-joins or multi-step logic, SQL window functions offer a streamlined way to track these transitions. For example, by pairing the LEAD() function with DATEDIFF(), you can instantly measure the gap between consecutive events and surface insights that would otherwise require far more complex queries.

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How to Unforce a Query Execution Plan in SQL Server

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When you’ve forced an execution plan in SQL Server’s query store and later want to allow the optimizer to choose plans freely again, you’ll need to unforce the plan. You might want to do this because you’ve fixed the underlying issue or the forced plan is no longer optimal. Whatever the reason, the solution is straightforward and easy.

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Generating Staggered Dates with SQL Server Window Functions

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SQL Server’s window functions open up some creative possibilities when you need to work with dates. One interesting pattern involves pairing DATEADD() with ROW_NUMBER() to automatically generate sequential dates based on your query results.

This technique gives you a flexible way to calculate dates dynamically without hardcoding values or maintaining separate date tables. This can be useful for doing things like building a scheduling system, creating test data with realistic timestamps, or just spacing events across a timeline.

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