Modify Stage - Handling NULLs

For handling the NULL value in columns we can use modify functions in DataStage instead of Transformer. Rather, I'll always prefer a transformer to avoid unnecessary effort which we put to setting up modify stage. But sometime to tune the job over performance we have to do that ;-)

The below Function Specification we have to use to Handle NULL --

Some more design tips for DataStage Job Development

• Common information like home directory, system date, username, password should be initialized in a global variable and then variable should be referred everywhere.

• Stage Variables allow you to hold data from a previous record when the next record, allowing you to compare between previous and current records. Stage variables also allow you return multiple errors for a record of information. By being able to evaluate all data in a record and not just error on the first exception that is found, the cleanup of data is more efficient and requires less iteration.

Datastage Coding Checklist

1. Ensure that the null handling properties are taken care for all the nullable fields. Do not set the null field value to some value which may be present in the source.
2. Ensure that all the character fields are trimmed before any processing. Normally extra spaces in the data may lead to some errors like lookup mismatch which are hard to detect.
3. Always save the metadata (for source, target or lookup definitions) in the repository to ensure re usability and consistency.