NULL behavior in datastage transformer

NULL - It is always a challenge for developers or architect while dealing with NULL in data. Every projects need some base rules to process NULLs.
     Today, we will see how the NULL behave in datastage, I hope, this will help you design a better job or flow to process the NULLs.

1. Null values can now be included in any expression

2. Null values no longer need to be explicitly handled

3. A null value in an expression will return a null value result. Example:
           1 + null = null
           "abc":null = null
           trim(null) = null

4. Exception: IsNull(null) and IsNotNull(null) will return true or false as expected

5. Any comparison involving null is unknown (i.e. is not true). Example:
          1 < null is not true
          1 >= null is not true
         null == null is not true
         null != null is not true

6. Logical operators - null is unknown (i.e. is not true)
        True AND null is not true
        True OR null is true
        False OR null is not true

7. As long as the target column is nullable records will not be dropped

8. Stage Variables are now always nullable

9. Stage Variable derivations can now include any input fields

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