What are the best practices for handling null values in Informatica PowerCenter?

1 Using ISNULL and IIF functions

The ISNULL function returns TRUE if the input value is null, and FALSE otherwise. You can use this function in expressions, filters, joins, and other transformations to check for null values and perform conditional logic. For example, you can use ISNULL to replace null values with a default value or flag them as errors.

The IIF function returns one of two values based on a condition. You can use this function to implement IF-THEN-ELSE logic in expressions and transformations. For example, you can use IIF to assign different values or actions based on whether the input value is null or not.

2 Using NULLIF and Nvl functions

The NULLIF function returns null if the two input values are equal, and the first value otherwise. You can use this function to convert certain values to null, such as blanks, zeros, or invalid codes. For example, you can use NULLIF to convert empty strings to null values before loading them to a target database.

The Nvl function returns the first input value if it is not null, and the second value otherwise. You can use this function to replace null values with a substitute value, such as a constant, a variable, or another column. For example, you can use Nvl to fill in missing values with the average or median of a column.

3 Using Lookup and Update Strategy transformations

The Lookup transformation allows you to look up values from a source or target table based on a matching condition. You can use this transformation to enrich the data with additional information from a reference table, validate the data against a master table and reject invalid values, or compare the data with a historical table and identify changes or updates. The Update Strategy transformation enables you to specify how to update or insert rows in a target table based on a condition. This transformation can be used to update only the non-null columns in a target table, insert new rows only if all the required columns are not null, and delete rows from a target table if any of the key columns are null.

4 Using Data Quality and Error Handling features

Informatica PowerCenter provides various features and options to improve the data quality and handle errors during the ETL process. You can use Data Quality transformations, such as Data Cleansing, Data Masking, and Data Validation, to standardize, protect, and verify the data. Additionally, you can use Error Handling options, such as Error Log, Reject File, and Workflow Recovery, to capture, store, and recover the errors. Moreover, you can employ Data Quality rules like Data Profiling, Data Auditing, and Data Quality Scorecard to measure, monitor, and report the data quality.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?