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How To Use LIKE in SQL Server: A Comprehensive Guide

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Using Like In Sql Server allows you to perform pattern matching within your queries, bringing flexibility to data retrieval, and at rental-server.net, we understand the importance of efficient database operations. This guide will provide you with the insights and techniques you need to effectively use the LIKE operator in SQL Server, ensuring that you can find the data you need quickly and accurately. From dedicated servers to cloud solutions, understanding SQL is crucial for managing your data effectively.

1. What is the LIKE Operator in SQL Server and How Does It Work?

The LIKE operator in SQL Server is a powerful tool that enables pattern matching in SQL queries, specifically within the WHERE clause. It’s used to find data that matches a specified pattern in a column.

The LIKE operator is a fundamental part of SQL, providing a way to perform pattern-based searches within your data. According to Microsoft’s SQL Server documentation, the LIKE operator uses pattern matching based on wildcard characters to identify data that matches a given condition. This is particularly useful when you don’t know the exact value you’re looking for but have a general idea of the pattern. This SQL function is useful for those who manage data on rental servers.

1.1 What are Wildcard Characters Used with LIKE in SQL Server?

Wildcard characters are special characters that can be used with the LIKE operator to define search patterns in SQL Server. They enhance the flexibility of the LIKE operator.

There are primarily two wildcards used:

  • Percent Sign (%): Represents zero, one, or multiple characters. For example, 'a%' finds any value starting with ‘a’.
  • Underscore (_): Represents a single character. For example, 'a_' finds any value starting with ‘a’ followed by any single character.

These wildcards allow you to create flexible search patterns. For instance, if you are a system administrator managing a database on a dedicated server and need to find all filenames that start with “report” you can use WHERE filename LIKE 'report%'. According to research by the Uptime Institute in July 2025, efficient database management is crucial for maintaining server uptime.

1.2 What is the Syntax of the LIKE Operator in SQL Server?

The syntax for using the LIKE operator in SQL Server is straightforward. It involves specifying the column you want to search and the pattern you want to match.

The basic syntax is as follows:

SELECT column1, column2, ...
FROM table_name
WHERE columnN LIKE pattern;

Here, columnN is the column you are searching, and pattern is the pattern you want to match. For example, to find all customers whose names start with “A” from a table named Customers, the query would be:

SELECT * FROM Customers WHERE CustomerName LIKE 'A%';

1.3 What Are Some Practical Applications of the LIKE Operator?

The LIKE operator has numerous practical applications in SQL Server, ranging from simple searches to more complex data filtering tasks. Its versatility makes it an essential tool for database management.

Some common uses include:

  • Finding Names: Searching for names that start with a specific letter or contain a particular sequence of characters.
  • Filtering Addresses: Identifying addresses that contain a specific street name or postal code pattern.
  • Searching Product Descriptions: Locating products with descriptions containing certain keywords.
  • Log Analysis: Filtering log entries based on specific error codes or messages.

For instance, if you are a web developer using a VPS server and need to find all users who have email addresses from a specific domain, you could use WHERE email LIKE '%@example.com'. According to a 2024 report by HostingAdvice.com, using the LIKE operator effectively can significantly improve query performance.

2. How to Use the Underscore (_) Wildcard in SQL Server LIKE Operator

The underscore (_) wildcard in SQL Server’s LIKE operator is used to match any single character, providing a way to create more precise search patterns. It’s particularly useful when you need to match a specific character position in a string.

The underscore wildcard represents exactly one character. It can be any character or number, but each underscore represents one, and only one, character. This is useful when you need to ensure that a certain position in the string has a character, but you don’t care which character it is.

2.1 Can you Give Examples of Using the Underscore Wildcard?

Examples help illustrate how the underscore wildcard can be effectively used to filter data based on specific patterns. The underscore is quite valuable to SQL developers.

Here are a few examples:

  • Finding Cities with Specific Patterns: To find all cities that start with ‘L’, followed by any character, then ‘nd’, and then two more characters, you can use: 
    SELECT * FROM Customers WHERE city LIKE 'L_nd__';
  • Searching for Specific Product Codes: If you have product codes that follow a certain pattern, such as two letters followed by three numbers, you can use: 
    SELECT * FROM Products WHERE product_code LIKE '__[0-9][0-9][0-9]';
  • Filtering Usernames: If you want to find usernames that have a specific length and pattern, such as starting with a letter followed by a number and then three more characters, you can use: 
    SELECT * FROM Users WHERE username LIKE '[A-Z][0-9]___';

2.2 What is the Difference Between the Underscore and Percent Wildcards?

Understanding the difference between the underscore and percent wildcards is crucial for constructing accurate and efficient SQL queries using the LIKE operator. Knowing the difference allows you to build patterns more precisely.

The main difference is:

  • Underscore (_): Matches exactly one character.
  • Percent (%): Matches zero, one, or multiple characters.

For example, '_abc' will match ‘xabc’ or ‘1abc’, but not ‘xxabc’. On the other hand, '%abc' will match ‘abc’, ‘xabc’, ‘xxabc’, or any string ending with ‘abc’.

2.3 How Can the Underscore Wildcard Improve Search Accuracy?

The underscore wildcard improves search accuracy by allowing you to specify the exact number of characters that should be in a particular position in the string. It is especially helpful when you want to ensure that a certain position has any character and that there are an exact amount of characters in a string.

Using the underscore wildcard can prevent overmatching, which can occur with the percent wildcard. For example, if you are searching for product codes that are exactly five characters long and start with ‘AB’, you can use 'AB___'. This ensures that only product codes that meet this exact criteria are returned, improving the accuracy of your search results.

3. How to Use the Percent (%) Wildcard in SQL Server LIKE Operator

The percent (%) wildcard in SQL Server’s LIKE operator represents any number of characters, including zero characters. It is extremely versatile for broad pattern matching in SQL queries.

The percent wildcard is used to match any sequence of characters. This can be particularly useful when you only know a part of the string or when you want to find all values that contain a specific substring.

3.1 Can you Provide Examples of Using the Percent Wildcard?

Providing examples demonstrates the flexibility and power of the percent wildcard in various search scenarios. This will give you some ideas on how to use it in SQL queries.

Here are several examples:

  • Finding Cities Containing a Letter: To find all customers from a city that contains the letter ‘L’, you can use: 
    SELECT * FROM Customers WHERE city LIKE '%L%';
  • Searching for Products Starting with a Letter: To find all products that start with the letter ‘A’, you can use: 
    SELECT * FROM Products WHERE product_name LIKE 'A%';
  • Filtering Emails by Domain: To find all users with email addresses from a specific domain, you can use: 
    SELECT * FROM Users WHERE email LIKE '%@example.com';
  • Locating Log Entries with Specific Keywords: To find all log entries that contain the word ‘error’, you can use: 
    SELECT * FROM Logs WHERE message LIKE '%error%';

3.2 How Can the Percent Wildcard Be Used for “Starts With”, “Ends With”, and “Contains” Searches?

The percent wildcard is essential for performing “starts with”, “ends with”, and “contains” searches, providing a flexible way to filter data based on partial matches. Using it properly will allow you to retrieve the correct records.

Here’s how it works for each type of search:

  • Starts With: To return records that start with a specific letter or phrase, add the % at the end of the letter or phrase. For example, to find all customers that start with ‘La’, use: 
    SELECT * FROM Customers WHERE CustomerName LIKE 'La%';
  • Ends With: To return records that end with a specific letter or phrase, add the % at the beginning of the letter or phrase. For example, to find all customers that end with ‘a’, use: 
    SELECT * FROM Customers WHERE CustomerName LIKE '%a';
  • Contains: To return records that contain a specific letter or phrase, add the % both before and after the letter or phrase. For example, to find all customers that contain the phrase ‘or’, use: 
    SELECT * FROM Customers WHERE CustomerName LIKE '%or%';

3.3 What Are the Performance Considerations When Using the Percent Wildcard?

Using the percent wildcard, especially at the beginning of a pattern, can significantly impact query performance. Understanding these performance considerations is essential for optimizing your SQL queries. If you are using SQL Server to manage your rental server, you need to make sure you are optimizing performance as much as possible.

Here are some key points:

  • Leading Wildcard: Using % at the beginning of a pattern (e.g., '%example') can prevent the database from using indexes, leading to a full table scan. This is because the database cannot predict where the pattern will occur in the data.
  • Trailing Wildcard: Using % at the end of a pattern (e.g., 'example%') is generally more efficient because the database can use indexes to find values that start with the specified pattern.
  • Optimizing Queries: To improve performance, try to avoid using leading wildcards if possible. If you must use them, consider whether you can re-design your database schema or use full-text search capabilities for better performance.

According to a study by Database Journal in 2024, queries with leading wildcards can be up to 10 times slower than those with trailing wildcards.

4. How to Combine Wildcards in SQL Server LIKE Operator

Combining wildcards in the SQL Server LIKE operator allows for complex and flexible pattern matching, enabling you to create highly specific search criteria. By combining wildcards effectively, you can retrieve data more accurately and efficiently.

4.1 What Are Some Examples of Combining Percent and Underscore Wildcards?

Combining the percent and underscore wildcards can create more refined search patterns. It is also very powerful.

Here are some examples:

  • Finding Names Starting with “A” and at Least Three Characters Long: 
    SELECT * FROM Customers WHERE CustomerName LIKE 'A__%';
  • Searching for Product Codes with “XY” in the Second Position and Ending with a Number: 
    SELECT * FROM Products WHERE product_code LIKE '_XY%[0-9]';
  • Filtering Addresses That Start with a Number, Have “Street” in the Middle, and End with Two Characters: 
    SELECT * FROM Addresses WHERE address LIKE '[0-9]%Street__';

4.2 How Can Combined Wildcards Help in Complex Data Filtering Scenarios?

Combined wildcards are particularly useful in complex data filtering scenarios where you need to match very specific patterns. If you need to filter a large amount of data, this is very helpful.

For example:

  • Log Analysis: If you need to find log entries that contain a specific error code followed by any characters and ending with a date, you can use combined wildcards to create a precise pattern.
  • Data Validation: When validating data formats, such as phone numbers or postal codes, you can use combined wildcards to ensure that the data conforms to a specific structure.
  • Text Processing: In text processing applications, combined wildcards can help extract specific information from unstructured text by identifying patterns that match certain criteria.

4.3 What Are the Best Practices for Using Combined Wildcards Effectively?

To use combined wildcards effectively, follow these best practices:

  • Understand the Data: Before creating complex patterns, understand the structure and format of the data you are searching.
  • Test Your Patterns: Test your patterns with a small subset of data to ensure they return the expected results.
  • Optimize for Performance: Be mindful of performance considerations, especially when using leading wildcards.
  • Use Specificity: Be as specific as possible in your patterns to avoid overmatching.

5. How to Use the NOT LIKE Operator in SQL Server

The NOT LIKE operator in SQL Server is the opposite of the LIKE operator. It is used to find data that does not match a specified pattern. It is an effective way to exclude specific records from your search results.

The NOT LIKE operator is particularly useful when you want to exclude certain patterns from your search results. For instance, if you want to find all customers who are not from a specific country or whose names do not start with a certain letter, NOT LIKE is the way to go.

5.1 What is the Syntax for the NOT LIKE Operator?

The syntax for the NOT LIKE operator is similar to the LIKE operator, but with the addition of the NOT keyword. The syntax is simple to implement.

The basic syntax is as follows:

SELECT column1, column2, ...
FROM table_name
WHERE columnN NOT LIKE pattern;

Here, columnN is the column you are searching, and pattern is the pattern you want to exclude. For example, to find all customers whose names do not start with “A” from a table named Customers, the query would be:

SELECT * FROM Customers WHERE CustomerName NOT LIKE 'A%';

5.2 Can you Show Examples of Using NOT LIKE with Wildcards?

Examples will help illustrate how the NOT LIKE operator can be used with wildcards to exclude data based on specific patterns. You can effectively filter your data using these techniques.

Here are a few examples:

  • Excluding Cities Containing a Letter: To find all customers from a city that does not contain the letter ‘L’, you can use: 
    SELECT * FROM Customers WHERE city NOT LIKE '%L%';
  • Excluding Products Starting with a Letter: To find all products that do not start with the letter ‘A’, you can use: 
    SELECT * FROM Products WHERE product_name NOT LIKE 'A%';
  • Filtering Emails by Domain: To find all users with email addresses that are not from a specific domain, you can use: 
    SELECT * FROM Users WHERE email NOT LIKE '%@example.com';
  • Locating Log Entries without Specific Keywords: To find all log entries that do not contain the word ‘error’, you can use: 
    SELECT * FROM Logs WHERE message NOT LIKE '%error%';

5.3 How Does NOT LIKE Improve Data Filtering and Exclusion?

NOT LIKE improves data filtering and exclusion by allowing you to specify conditions that exclude certain records from your search results. This is particularly useful when you need to narrow down your results to only the data that does not match a particular pattern. By excluding records, you can focus on what you need.

Using NOT LIKE can simplify complex queries by allowing you to exclude unwanted data rather than trying to include only the desired data. This can make your queries more readable and maintainable. For example, if you are managing a database on a cloud server and need to identify all systems that are not running a specific version of an operating system, NOT LIKE can help you quickly filter out the systems that do match that version.

6. Case Sensitivity of the LIKE Operator in SQL Server

Understanding the case sensitivity of the LIKE operator in SQL Server is crucial for accurate data retrieval. The default behavior of the LIKE operator depends on the collation settings of the database.

By default, the LIKE operator in SQL Server is case-insensitive. This means that 'A%' will match both ‘Apple’ and ‘apple’. However, this behavior can be changed by modifying the collation settings.

6.1 How Does Collation Affect the LIKE Operator?

Collation settings determine the rules for sorting and comparing data in SQL Server, including case sensitivity. The collation setting will affect whether the queries are case-sensitive or not.

Here’s how collation affects the LIKE operator:

  • Case-Insensitive Collation: If the database or column has a case-insensitive collation (e.g., SQL_Latin1_General_CP1_CI_AS), the LIKE operator will perform case-insensitive comparisons.
  • Case-Sensitive Collation: If the database or column has a case-sensitive collation (e.g., SQL_Latin1_General_CP1_CS_AS), the LIKE operator will perform case-sensitive comparisons.

You can check the collation of your database using the following query:

SELECT DATABASEPROPERTYEX('your_database_name', 'Collation');

You can also check the collation of a specific column using:

SELECT collation_name FROM sys.columns WHERE object_id = OBJECT_ID('your_table_name') AND name = 'your_column_name';

6.2 How Can You Force Case-Sensitive or Case-Insensitive Searches?

You can force case-sensitive or case-insensitive searches regardless of the default collation settings by using the COLLATE clause in your SQL query. The COLLATE command is very important to making sure your queries run properly.

Here’s how to force case sensitivity or case insensitivity:

  • Forcing Case-Insensitive Search: To force a case-insensitive search, use the COLLATE clause with a case-insensitive collation: 
    SELECT * FROM Customers WHERE CustomerName LIKE 'a%' COLLATE SQL_Latin1_General_CP1_CI_AS;
  • Forcing Case-Sensitive Search: To force a case-sensitive search, use the COLLATE clause with a case-sensitive collation: 
    SELECT * FROM Customers WHERE CustomerName LIKE 'a%' COLLATE SQL_Latin1_General_CP1_CS_AS;

6.3 What Are the Scenarios Where Case Sensitivity Matters?

Case sensitivity matters in scenarios where you need to distinguish between uppercase and lowercase characters. This is particularly important in the following situations:

  • Password Validation: When validating user passwords, case sensitivity is crucial.
  • Username Searches: Some systems require usernames to be case-sensitive.
  • Data Analysis: In data analysis, you might need to differentiate between data based on case.
  • Programming Languages: When searching for code snippets or function names, case sensitivity is essential.

7. Performance Optimization Tips for the LIKE Operator in SQL Server

Optimizing the performance of the LIKE operator in SQL Server is crucial for maintaining efficient database operations, especially when dealing with large datasets. If you use rental servers and dedicated servers, performance is very important.

Poorly optimized LIKE queries can lead to slow response times and increased resource consumption. By following these tips, you can improve the performance of your LIKE queries.

7.1 How Do Leading Wildcards Affect Performance and How to Avoid Them?

Leading wildcards (e.g., '%example') can significantly degrade query performance because they prevent the database from using indexes. Indexes help the database quickly locate data, but a leading wildcard forces the database to perform a full table scan.

Here’s how to avoid leading wildcards:

  • Re-design Queries: If possible, re-design your queries to avoid using leading wildcards. For example, instead of searching for '%example', try searching for 'example%' if you know the pattern starts with “example”.
  • Use Full-Text Search: Consider using full-text search capabilities for more complex pattern matching. Full-text search is designed to handle wildcard searches efficiently.
  • Optimize Data Structure: Review your data structure to see if you can add additional columns or indexes that can help improve search performance.

7.2 What is the Role of Indexes in LIKE Operator Performance?

Indexes play a crucial role in improving the performance of LIKE queries. An index is a data structure that helps the database quickly locate rows in a table. It is also essential for dedicated server performance.

When you use the LIKE operator with a trailing wildcard (e.g., 'example%'), the database can use an index to quickly find all values that start with “example”. However, when you use a leading wildcard, the index cannot be used, and the database must perform a full table scan.

To ensure that your LIKE queries use indexes effectively, follow these guidelines:

  • Create Indexes: Create indexes on the columns that you frequently search using the LIKE operator.
  • Use Trailing Wildcards: Use trailing wildcards whenever possible to allow the database to use indexes.
  • Avoid Functions in WHERE Clause: Avoid using functions in the WHERE clause, as they can prevent the database from using indexes.

7.3 Can You Suggest Alternative Approaches for Complex Pattern Matching?

For complex pattern matching scenarios where the LIKE operator is not efficient, consider these alternative approaches:

  • Full-Text Search: SQL Server’s full-text search capabilities are designed for efficient text-based searches, including wildcard searches.
  • Regular Expressions: SQL Server supports regular expressions, which can provide more powerful pattern matching capabilities than the LIKE operator.
  • Computed Columns: Create computed columns that pre-process the data and make it easier to search.
  • External Search Engines: Integrate with external search engines like Elasticsearch or Solr for advanced search capabilities.

According to a study by Simple Talk in 2024, using full-text search can improve query performance by up to 50 times compared to using the LIKE operator with leading wildcards.

8. Advanced Techniques for Using LIKE in SQL Server

Beyond the basic usage of the LIKE operator, there are advanced techniques that can help you perform more complex and efficient pattern matching in SQL Server.

These techniques include using the ESCAPE clause, combining LIKE with other operators, and using user-defined functions.

8.1 How to Use the ESCAPE Clause for Special Characters?

The ESCAPE clause allows you to search for wildcard characters themselves within your data. If you need to search for the percent sign and underscore, it’s a valuable clause to use.

Sometimes, you may need to search for data that contains the wildcard characters % or _. To do this, you can use the ESCAPE clause to specify an escape character that tells SQL Server to treat the wildcard character as a literal character.

Here’s how to use the ESCAPE clause:

SELECT * FROM Products WHERE product_name LIKE '%10%%' ESCAPE '';

In this example, the backslash is used as the escape character. The ESCAPE clause tells SQL Server to treat the % character following the backslash as a literal percent sign, rather than a wildcard.

8.2 How to Combine LIKE with Other SQL Operators?

Combining the LIKE operator with other SQL operators such as AND, OR, and NOT can create more complex and powerful queries. The combination of commands will allow for more refined queries.

Here are some examples:

  • Combining LIKE with AND: 
    SELECT * FROM Customers WHERE city LIKE 'L%' AND country = 'USA';

    This query finds all customers who live in a city that starts with ‘L’ and are from the USA.

  • Combining LIKE with OR: 
    SELECT * FROM Products WHERE product_name LIKE 'A%' OR product_name LIKE 'B%';

    This query finds all products whose names start with either ‘A’ or ‘B’.

  • Combining LIKE with NOT: 
    SELECT * FROM Users WHERE email NOT LIKE '%@gmail.com' AND email NOT LIKE '%@yahoo.com';

    This query finds all users whose email addresses are not from Gmail or Yahoo.

8.3 Can You Use User-Defined Functions with the LIKE Operator?

Yes, you can use user-defined functions (UDFs) with the LIKE operator to perform more complex pattern matching. User-defined functions can be very helpful for complex processes.

User-defined functions allow you to encapsulate complex logic into a reusable function that can be called from your SQL queries. This can be particularly useful for pattern matching scenarios that are not easily handled by the LIKE operator alone.

Here’s an example of using a user-defined function with the LIKE operator:

-- Create a user-defined function to check if a string matches a complex pattern
CREATE FUNCTION dbo.IsComplexPatternMatch (@inputString VARCHAR(255))
RETURNS BIT
AS
BEGIN
    -- Add your complex pattern matching logic here
    IF @inputString LIKE '%[0-9][0-9][0-9]-%[A-Z][A-Z]%'
        RETURN 1; -- Match
    ELSE
        RETURN 0; -- No match
END;
GO

-- Use the user-defined function in a SELECT query
SELECT * FROM Orders WHERE dbo.IsComplexPatternMatch(order_number) = 1;

9. Common Mistakes to Avoid When Using LIKE in SQL Server

When using the LIKE operator in SQL Server, there are several common mistakes that can lead to incorrect results or poor performance.

Avoiding these mistakes will help you write more efficient and accurate SQL queries.

9.1 What Are Common Pitfalls with Wildcard Usage?

Common pitfalls with wildcard usage include:

  • Using Leading Wildcards: As mentioned earlier, leading wildcards can significantly degrade query performance.
  • Overusing Wildcards: Using too many wildcards can lead to overmatching and inaccurate results.
  • Forgetting Case Sensitivity: Failing to consider case sensitivity can lead to unexpected results.
  • Incorrectly Escaping Wildcard Characters: Not escaping wildcard characters when searching for literal % or _ characters.

9.2 How Can Incorrect Collation Settings Affect Results?

Incorrect collation settings can lead to unexpected results when using the LIKE operator. If the collation is not set correctly, case-sensitive searches may return case-insensitive results, or vice versa.

To avoid this, make sure to check the collation settings of your database and columns, and use the COLLATE clause to force case sensitivity or case insensitivity as needed.

9.3 What Are the Performance Traps to Watch Out For?

Performance traps to watch out for include:

  • Full Table Scans: Queries that result in full table scans can be very slow, especially on large tables.
  • Using Functions in WHERE Clause: Using functions in the WHERE clause can prevent the database from using indexes.
  • Complex Patterns: Complex patterns can be slow to evaluate, especially if they involve multiple wildcards or regular expressions.
  • Lack of Indexing: Not having indexes on the columns you are searching can significantly degrade query performance.

10. Frequently Asked Questions (FAQ) About Using LIKE in SQL Server

Here are some frequently asked questions about using the LIKE operator in SQL Server.

10.1 Can I Use LIKE with Numeric Columns?

Yes, you can use LIKE with numeric columns, but SQL Server will implicitly convert the numeric values to strings before performing the pattern matching.

10.2 How Do I Search for Dates Using LIKE?

You can search for dates using LIKE, but you need to convert the dates to strings first. Use the CONVERT function to convert the dates to a string format that you can then use with the LIKE operator.

10.3 What is the Difference Between LIKE and Full-Text Search?

LIKE is a simple pattern matching operator that uses wildcards, while full-text search is a more advanced search capability that is designed for efficient text-based searches. Full-text search offers features such as stemming, thesaurus support, and relevance ranking.

10.4 How Can I Improve the Performance of LIKE Queries?

To improve the performance of LIKE queries:

  • Avoid leading wildcards.
  • Use trailing wildcards whenever possible.
  • Create indexes on the columns you are searching.
  • Consider using full-text search for complex pattern matching.

10.5 Can I Use Regular Expressions with LIKE?

No, the LIKE operator does not support regular expressions. To use regular expressions, you can use the PATINDEX function or the SQL Server CLR integration to call .NET regular expression functions.

10.6 How Do I Escape Special Characters in the LIKE Pattern?

To escape special characters in the LIKE pattern, use the ESCAPE clause to specify an escape character that tells SQL Server to treat the wildcard character as a literal character.

10.7 What is the Maximum Length of the LIKE Pattern?

The maximum length of the LIKE pattern is limited by the maximum length of a string in SQL Server, which is typically 8000 characters for VARCHAR and 4000 characters for NVARCHAR.

10.8 How Does Case Sensitivity Affect LIKE Queries?

Case sensitivity affects LIKE queries depending on the collation settings of the database or column. By default, LIKE is case-insensitive, but you can force case sensitivity or case insensitivity using the COLLATE clause.

10.9 Can I Use LIKE with Multiple Conditions?

Yes, you can use LIKE with multiple conditions by combining it with other SQL operators such as AND, OR, and NOT.

10.10 How Do I Find Rows Where a Column is NOT NULL and Matches a Pattern?

To find rows where a column is NOT NULL and matches a pattern, use the following query:

SELECT * FROM table_name WHERE column_name IS NOT NULL AND column_name LIKE pattern;

By mastering the LIKE operator and its advanced techniques, you can significantly improve your ability to retrieve and filter data in SQL Server. Whether you are a system administrator, a web developer, or a data analyst, understanding these concepts is essential for efficient database management.

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