Validate

Validate your data meets expectations

This tool is currently in Beta and is still being tested. Want to learn more? Like to provide feedback? Please reach out to [e-mail]

Validate that specified conditions are met in a given dataset.

Input/Output

Inputs	Outputs
Table to be validated	Validation Results Validated Table

Options

Inputs	Description
Validation Type	The type of validation being performed
Validation Name	The name of the validation being performed
Validation Description	A description of the validation being performed
Validation Field (If applicable)	The field from the validation table to perform said validation on
Expression (If applicable)	The expression used to validate data
Format Check (If applicable)	The format expected of a given column of data
Reference Table (If applicable)	The reference table used to validate data
Reference Field (If applicable)	The field from the reference table used to validate data
Aggregate Validation Results	A boolean value determining whether all of the validations must "Pass" in order for the aggregated validation result to "Pass". If set to true, the validation tool will produce a single validation column on output. If set to false, each validation created will produce a unique column of results.

Please Note: Depending on the validation type selected, different options will be displayed to the user.

Validation Type Options

Validation Type	Description
Look Up Check	Ensure that the values within a specified column from the validation table are found within a specified column from the reference table
Uniqueness Check	Ensure that a specified column of values from the validation table are unique
Expression Check	Use Cascade's expression builder to define your own validation
Format Check	Ensure that a specified column of values from the validation table match a specified format
Null Check	Check whether the values in a given column are null
Data Type Check	Check that the values in a given column are of a certain datatype

Expression Check Example

Let's say that I want to ensure that my price data contains only positive values. I could use an expression check to do so.

The first output from this tool is seen within the Validation Results tab in the image above. This tool also produces a new column in your table, as seen below.

Look Up & Formatting Check Example

Let's say we have new product data that we want to add to our existing master data set. We need to ensure that all products in the new dataset exist within our master dataset.

Now let's say we additionally need to ensure that our SKUs within this dataset are entirely unique.

As seen above, we have two validations occurring within the same tool. The Aggregate Validation Results* checkbox has been left unchecked, resulting in two unique validation columns in our resultant table.

If, however, we want to force both columns to pass in order for our validation to pass, we can check the Aggregate Validation Results* checkbox as follows, producing a single column in our resultant table.

Format Check Example

Let's say you have new data containing email addresses and you must validate whether or not those addresses are in a valid email format.