Pro Tips for Data Validation in SQL

In the real world, we collect data from multiple sources and store it in a database, and this data may not always meet our standards or requirements. To tackle this, SQL provides powerful tools to ensure that the data is valid, complete, and in the correct format. In this article, let us discuss how SQL can be used for data validation. Pro Tips for Data Validation in SQL

Data Validation

Data validation is essential for maintaining the quality and integrity of the database. When the data is stored in a database, it should adhere to certain constraints. Whether we are ensuring that an email address is correctly formatted, checking for missing values, or validating that numerical values fall within a certain range, SQL helps to tackle these tasks.

Now, let us see some common data validation scenarios in SQL, along with examples wherever possible. I hope these pro tips for data validation in SQL will help you a lot in your learning.

Common Data Validation Scenarios in SQL

1. Ensuring Correct Email Format

In many applications, the common requirement is ensuring that email addresses follow a valid format. SQL can help check whether an email follows a typical pattern. SQL does not provide built-in regular expressions in all databases, but certain databases, such as MySQL, support the REGEXP operator, which allows to validate patterns like email addresses.

SQL Query:

” SELECT email FROM Users
WHERE email NOT REGEXP ‘^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}$’; ”

• This query uses Regular expression, and any records that do not match the pattern will be returned so that we can fix the invalid entries

2. Checking for Missing or Null Values

Incomplete data or missing data is a common issue in databases. Some columns may allow NULL values, but we need to ensure that critical fields like first_name or mobile_number should not be NULL. SQL can help us to identify records that are missing important information. Here is how we can write a query for this.

SQL Query:

” SELECT * FROM users
WHERE first_name IS NULL OR email  IS NULL; ”

• This query retrieves rows where the first_name or email columns contain NULL values.

3. Data length constraints

Sometimes, it’s important to validate that data in a column meets a specific length requirement. For example, a product code might need to be exactly 8 characters long, or a phone number should be in 10 digits.

SQL Query:

SELECT mobile_number FROM users
WHERE LENGTH(mobile_number)  != 10;

• This query checks if any mobile number does not contain exactly 10 characters/digits.

4. Validating numerical ranges

When working with numerical data, we need to ensure that values fall within an acceptable range. If we consider the customer’s age as an example, age should be between 18 and 110 years. or a product price should not be negative. Here is the query to solve this issue.

SQL Query:

SELECT price FROM products
WHERE price < 0;

• This query finds any products with a negative price, which is not valid.

5. Checking Duplicate Records

Sometimes, data might be duplicated due to errors during data entry or import. We can use SQL to find and remove duplicates based on certain criteria, such as matching names and email addresses. Let us write a query for this.

SQL Query:

SELECT email, count(*) FROM users
GROUP BY email
HAVING COUNT(*) > 1;

• This query identifies duplicate email addresses in the users table.

 

Conclusion:

Data validation and quality checks are essential in data management in SQL. Whether we are checking for missing values, length constraints, validating numerical ranges, or checking email formats, SQL provides a set of powerful tools to maintain high-quality data in the database.

I hope that this article will help you learn how to maintain data in the database and to perform data validation operations with SQL. If you have any queries, please reach out to us through email or our social media handles.

 

Thank You

-Vinay neeradi (techy_miki)