What are the most common data validation rules for relational databases?

Common data validation rules for relational databases include ensuring data types match (e.g., integers for integers), limiting string lengths, validating numeric ranges, checking formats (e.g., dates), requiring non-null values, enforcing uniqueness (e.g., primary keys), verifying referential integrity with foreign keys, maintaining consistency between related data, restricting values to predefined lists, and implementing custom business rules.

The data type is very important to store the proper data and to be sure we don't get any errors. Relational databases like Oracle has evolved a lot, and it is possible to store any kind of data over any kind of file, for example: working with JSON data or creating external tables over parquet files. The more flexibility you have, more careful you have to be. "With great power comes great responsibility"

Data validation rules for relational databases often focus on ensuring data integrity, consistency, and accuracy. These rules include entity integrity, which ensures that each row in a table is uniquely identifiable, typically through a primary key. Referential integrity ensures that relationships between tables remain consistent, meaning that for every foreign key in a table, there should be a matching primary key in its linked table. Domain integrity involves validating that data entries fall within a specific range, length, or pattern. Other common rules include checking for null values where data is required, ensuring data type consistency, and validating that data matches predefined formats or patterns.

Common data validation rules for relational databases regarding formats and patterns include: Date Validation Email Validation Phone Number Validation Postal Code Validation Social Security Number (SSN) Validation Credit Card Number Validation URL Validation Custom Pattern Validation

Also this has evolve, and more over the Oracle Database. It is possible to use SQL Domains. A SQL domain is a dictionary object that belongs to a schema and encapsulates a set of optional properties and constraints for common values, such as credit card numbers, registration plates or email addresses. You can ensure the right data just defining the data type :)

Here are the data validation rules I use: - Data Type Validation: It is crucial that data inputted into a field matches the designated data type (e.g integer, string, date). - Length Validation: To maintain data integrity, the length of input data for fields like strings or text can be limited. - Numeric values are checked to make sure they fall within a specific range. - Mandatory fields must have some input to avoid incomplete entries. - Uniqueness Validation: It is necessary to validate that a value is unique within a particular context, usually for primary keys or unique constraints. - Data is validated against a specific format, such as ensuring that an email address is correctly entered.

As mentioned on the point before, using Oracle Database SQL Domains you have the full flexibility. For example: CREATE DOMAIN voucher AS Number DEFAULT ON NULL 0 CONSTRAINT voucher_limits CHECK (voucher > = 0 and voucher <=500) ENABLE DISPLAY TO_CHAR(voucher, '$999.99') ORDER ( -1*voucher) ANNOTATIONS (properties '{"Department": "Marketing", "Country": "USA", "Max Voucher": "500$" }'); And then you can create the table using this domain: create table customers ( first_name varchar2(100), last_name varchar2(100), cust_id number, customer_voucher number domain voucher);

For transactional applications, it is very important to define all the "restrictions" like primary key and foreign key. However, it is not always recommended for DW use cases

Desde un punto de vista de validación de datos: [1] Las llaves sirven para ver si se respeta una relación entre otras entidades con el fin de extraer información completa del registro original. La forma en como se gestiona esta relación es importante, porque esto afecta las actualizaciones del contenido de la información. [2] Las Restricciones son para asegurar que el campo no tenga un valor o comportamiento invalido; por ejemplo, en postgres tiene los comandos NOT NULL, CHECK or UNIQUE para ello, haciendo que la calidad de datos puede integrarse mejor en la parte fisica.

At first glance, triggers and functions seem like they can solve all your problems. And, to be sure, they are incredibly powerful tools that absolutely belong in every database designer’s toolkit! But … they should also be used sparingly, with caution — especially for validation. Consider a seemingly-ideal candidate: ensuring that only valid email addresses wind up in a given field — especially since the only way to be sure an address is valid is to send an email. Are you willing to throw an error because a valid address in a form you didn’t consider is entered? Will the resulting error message tell the next developer where to fix the problem?

The Consistency Validation rule checks for consistency across related data. For example, if you have a “birth date” field and an “age” field, consistency validation ensures that the age matches the birth date. Inconsistencies can lead to inaccuracies in reporting and analysis