• This function generates values of Decimal type with fifteen digits of precision after the decimal point. If you want to see all digits in the generated value, you might need to apply a different number format. See NUMFORMAT Function.
• New random numbers are generated within the browser, after each browser refresh, and between subsequent job executions.

Optionally, you can insert an integer as a parameter.

• When this value is present, this seed value is used as part of the random number generator such that its output is a set of pseudo-random values, which are consistent between job executions.
• When the browser is refreshed, the random numbers remain consistent when the seed value is present.
• This value must be a valid literal Integer value. For more information on valid values, see Integer Data Type.
• If none is provided, a seed is generated based on the system timestamp.

Column references or functions returning Integer values are not supported. 

Example:

Output: For each row, generate a random number between 0 and 1 in the new random function.

Example with seed value:

Output: For each row, generate a random number between 0 and 1 in the new random function. The generated random set of random values are consistent between job executions and are, in part, governed by the seed value 2.

There are no arguments for this function.

int_value

Optional Integer literal that is used to generate random numbers. Use of a seed value ensures consistency of output between job executions.

• This value must be a valid literal Integer value. For more information on valid values, see Integer Data Type.
  
• Literal numeric values should not be quoted. Quoted values are treated as strings.
• Multiple columns and wildcards are not supported.

Example - Random values

In the following example, the random column is generated by the RAND function:

Example - Type check functions

The RAND function is typically used to introduce randomness of some kind in your data. In the following example, it is used to perform sampling within your wider dataset. 
Source:

You want to extract a random sample of 20% of your set of orders for further study:

Transformation:

You can use the following transform to generate a random integer from one to 10:

You can now use the following transform to keep only the rows that contain random values that are in the top 20%, where the value is 9 or 10:

Results: