The `!=` or `<>` operator in SQL is used to check if two values are not equal. This is crucial for filtering data and performing conditional logic in queries. It's a fundamental comparison operator.
The `!=` (not equal to) or `<>` (not equal to) operator is a fundamental comparison operator in SQL. It's used to identify rows where a specific column's value does not match a given value. This is essential for filtering data based on conditions. For instance, you might want to select all customers who haven't placed an order in the last month. Or, you might want to find all products whose price is not equal to $10. The `!=` and `<>` operators are interchangeable in most SQL dialects, although `!=` is more common in some systems. Using these operators allows for precise data selection and manipulation. They are crucial for building complex queries that require specific criteria for data retrieval.
The `!=` operator is critical for filtering data in SQL. It allows developers to extract specific subsets of data based on non-equality conditions. This is essential for tasks like reporting, data analysis, and data manipulation.
!=
and <>
exactly the same in SQL, and do all databases accept both?Yes—functionally they both mean “not equal to.” ANSI-SQL formally defines <>
, while most popular engines (MySQL, PostgreSQL, SQL Server, Snowflake, etc.) also accept !=
. If you need guaranteed cross-database portability, use <>
; otherwise choose whichever your team’s style guide prefers.
!=
to filter data?To list every product whose price is not $10 you could run:SELECT product_id, name, price FROM products WHERE price != 10;
The same pattern works for any column—e.g., status <> 'inactive'
to exclude inactive rows.
Galaxy’s AI-powered SQL editor auto-completes comparison operators, highlights ANSI-incompatible syntax, and suggests parameterized patterns like price <> :target_price
. Because the copilot understands your schema, it offers context-aware hints and instantly previews results—so you spend less time debugging and more time analyzing data.