A “columns mismatch” error in SQL usually happens when the number of values you’re trying to insert doesn’t line up with the number of columns in the table. It’s not a complicated issue, but it can be an easy one to overlook, especially when working with tables that evolve over time or when you skip specifying column names in your INSERT statements.
Understanding why the error occurs makes it simple to avoid, and a few small habits can help keep your SQL inserts clean and reliable.
If you’ve written SQL long enough, you’ve probably run into the dreaded “Divide by zero error encountered” message. This happens when you try to perform a division and the denominator turns out to be zero. SQL Server throws an error immediately, which stops your query. This can be annoying, especially if the zero values are expected in the data but you don’t want them breaking your query.
One simple way to deal with this is by using the NULLIF() function.
When you work with dates in SQL Server, you’ll often run into situations where a value can’t be converted directly to a datetime or date. This usually happens because the source data isn’t in a format SQL Server recognises, or because the value itself is out‑of‑range (e.g., “2025‑02‑30”). Fortunately, the built‑in conversion functions CAST() and CONVERT() provide us with enough flexibility to clean up those problematic values without resorting to messy string manipulation.
Below we’ll look at the most common scenarios, show how to diagnose the issue, and demonstrate how to fix it.
There are a couple of obvious reasons you might be seeing an error that reads something like “The specified schema name “XYZ” either does not exist or you do not have permission to use it” in SQL Server. This typically happens when creating an object on a schema that doesn’t exist or you don’t have permissions.
This article shows an example of the error and a couple of ways to fix it, depending on the underlying cause.
Importing data into SQL Server is usually quite straightforward. That is, until you run into date and time formatting issues. Dates that look fine in a CSV, Excel, or flat file can suddenly throw errors or, worse, silently load with the wrong values. Since SQL Server is strict about how it interprets dates, mismatches between source file formats and SQL Server’s expectations are one of the most common headaches during imports.
This article looks at why these errors happen, what SQL Server expects, and how to troubleshoot these pesky date format issues.
If you’ve worked with SQL Server long enough, you’ve probably run into the dreaded “Only one expression can be specified in the select list when the subquery is not introduced with EXISTS” error. This error usually pops up when you try to use a subquery in a place where SQL Server expects a single value, but your query is returning multiple columns or multiple rows.
It’s a simple mistake, but it can be frustrating until you understand why it happens and how to fix it.
If you’ve worked with SQL Server for a while, you’ve probably run into the dreaded 4101 error that looks something like Msg 4104, Level 16, State 1, Line X: The multi-part identifier “X.Y” could not be bound.
It’s one of those vague errors that doesn’t immediately tell you what’s wrong. Basically SQL Server is complaining because it doesn’t know how to resolve the reference you wrote. This is usually a column or alias.
Let’s take a look at the most common causes, with examples to make them easier to spot.
When you’re working with SQL Server, sometimes you don’t just want to throw an error. Sometimes you want to build a message you can actually use elsewhere. That’s where FORMATMESSAGE() comes in. Instead of immediately printing a message like RAISERROR does, FORMATMESSAGE() gives you the formatted string back so you can decide what to do with it. This could include logging it, storing it, displaying it, or simply passing it along.
In simple terms, you can think of it as a way to take a predefined message from sys.messages (or even a custom string you provide) and turn it into a neatly formatted output. This can be quite handy when you need more control over how messages are handled in your SQL workflows.
Data conversion errors can be a frequent source of frustration when working with databases. And SQL Server is no exception. Such errors can interrupt workflows and lead to inconsistent results. While data conversion errors often happen during explicit conversions, they aren’t unique to this. Oftentimes the error can be due to an implicit conversion.
This article outlines five of the most common data conversion errors and provides practical steps to avoid them.
DuckDB offers two primary functions for type conversion: cast() and try_cast(). While they serve similar purposes, their behavior when handling invalid conversions differs significantly, which can greatly impact our data processing workflows.