We can use the Expression transformation to.

Example of expression transformation in informatica. 2 - create an output or variable port in your mapplet with datatype of integer. The TRUE result 1 is an integer and the FALSE result 3333 is a decimal. Enter an expression in a port that uses the value of data from an input or inputoutput port.

We can derive new information new data in expressions which let you modify individual ports of a single row or add or suppress them. Transformation language functions are SQL-like functions that transform data. Calculating annual Salary concatenation.

You can enter one or more characters. Expression transformation can also be used to test conditional statements before passing the data to other transformations. For example Sorter transformation.

Rogermass Dec 5 2013 1135 AM in response to snawsher 1 - create a parameter in your mapplet named ACCOUNT_LST and make sure to select TRUE for the last column is_expr_var. Create a new database target table for example say avg_mks_deptwise. The Expression Transformation in Informatica is a passive transformation that is used to perform non-aggregate calculations on the source data.

Expression Transformation in Informatica is a passive transformation can be used to calculate values in a single row. Expression is a Passive connected transformation used to calculate values in a single row before you write to the target. Informatica transformation can be created using Designer tools such as Mapping Designer Transformation developer and Mapplet Designer then configure the transformation by adding ports properties groups expressions and son on and finally link the transformation to other transformation and target definitions by drag and drop method in the mapping or mapplet.

Informatica Transformations are repository Objects that are used to perform aggregations sorting merging modifying distributing etc while passing data through them. Create a New mapping m_ avg_mks_deptwise. Ie concatenation division multiplication nothing but all your SQL level row functions can be applied here.