Data validation is an essential part of any data handling task whether you’re in the field collecting information, analyzing data, or using data. If data isn’t accurate from the start, your results definitely won’t be accurate either.
When developing API’s ensuring that all data you receive as input is as correct as possible and that it confirms to the intended rules.
Validating the accuracy, clarity, and details of data is necessary to mitigate any project defects. Without validating data, you run the risk of basing decisions on data with imperfections that are not accurately representative of the situation at hand.
While verifying data inputs and values is important, it is also necessary to validate the entity model itself. If the entity model is not structured or built correctly, you will run into issues when trying to use data in various applications and software.
The most straightforward and essentials rules used in data validation are rules that ensure data integrity. These are rules that check for:
- Minimum & Maximum lengths of fields
- Unique values
- Data types
- Consistent expressions
- No null values
Validating the structure of data is just as important as validating the data itself. Doing so will ensure that you are using the appropriate data model for the formats that are compatible with the applications you would like to use data in.
The API Template Pack provides capability to validate data at a number of points within the typical Request/Response pipeline.