Datastage Error and reject record Handling simplified

  The error and reject capturing is one of the best practices that I recommend to all developers, let me explain in a very simple way

Rejects: Rows that fail active or passive business rule give in the job design

Error: Rows which contains bad data, such as values too large for a column or text

Let’s look at an example here

Consider the following source file

Row id	Emp FName	Emp LName	EMP No	EMP Dep
1	John	Beck	2001	A
2	Mike	Morry	2002	A
3	Kevin	Peter	2003	B
4	Steve	Morry	2004	B
5	Jim	Chen	abcd	A

As per the business requirement, only Employee department “A” information’s should be loaded in the target table and Employee Number should number

Reject:

So these two records will be in the reject file as it’s Emp Dep is “B”

Row id	Emp FName	Emp LName	EMP No	EMP Dep
3	Kevin	Peter	2003	B
4	Steve	Morry	2004	B

Error:

This particular record will be in the error file as the Emp No is non-numeric

Row id	Emp FName	Emp LName	EMP No	EMP Dep
5	Jim	Chen	abcd	A

As a best practice, we should load the error record in to a table for further analysis or reporting purpose. Here is one sample layout which contains

Error Id	Error F Name	Error S name	EMP No	Error Dte
10002	Emp No	Input File  Name	abcd	12/12/2011

1)     A unique error record number

2)     Error Field name

3)     Error Source File or Table(if you are extracting from a raw table) name

4)     Error Field Name

5)     Date when the record processed

Here is how it will look in the Datastage