Explain filtering data and validating data

The actual process of data cleansing may involve removing typographical errors or validating and correcting values against a known list of entities.The validation may be strict (such as rejecting any address that does not have a valid postal code) or fuzzy (such as correcting records that partially match existing, known records).Finally, the primary key constraint is a mixture of the unique constraint and the not null constraint meaning the no two rows can have the same values in their columns and that a column must have data.Data cleansing or data cleaning is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data.No "ties" are allowed and the respondent can only pick only one option.Single select questions are appropriate when: A few examples of single select questions are yes-no, select best that applies, or preference scales.In this article, we will be addressing the different question types available with e Surveys Pro and how to use them in creating surveys.We will also discuss the different data validation techniques that you can use to optimize results.

For more information, see How to: Add an Entity to a Model. The name of the entity and the name of the class that represents the entity do not have to match.When using SQL, data validation is the aspect of a database that keeps data consistent but is very stupid to use. The key factors in data integrity are constraints, referential integrity and the delete and update options.The main types of constraints in SQL are check, unique, not null, and primary constraints.If the attribute marks a controller, the action filter applies to all action methods in that controller.Data validation is often a topic of great importance when it comes to databases.