Error Table Structure FAQS

1. What is the purpose of the E$ error table?

• The E$ error table stores detailed information about records that violate data quality constraints during processing. It helps track and manage errors, allowing users to identify and address issues with the data.

 

2. What does the ERR_TYPE column indicate?

• The ERR_TYPE column specifies the type of error based on the validation method used. It shows:
• 'F' for errors detected during flow control checks.
• 'S' for errors detected during static control checks.

 

3. What kind of information is stored in the ERR_MESS column?

• The ERR_MESS column contains the error message related to the violated constraint. It provides a detailed explanation of why a record was rejected, such as specific data validation failures.

 

4. Why is the CHECK_DATE column important?

• The CHECK_DATE column records the exact date and time when the datastore was checked. This helps track when data quality checks were performed and can be useful for auditing and troubleshooting.

 

5. What is the ORIGIN column used for?

• The ORIGIN column identifies the origin of the check operation. It can either refer to the datastore name or the mapping name and ID, depending on how the check was executed. This helps to trace where the check originated from.

 

6. What does the CONS_NAME column represent?

• The CONS_NAME column stores the name of the violated constraint. This column provides insight into which specific constraint (e.g., primary key, foreign key) the record failed to meet.

 

7. How is the CONS_TYPE column used in the error table?

• The CONS_TYPE column specifies the type of constraint that was violated. It can have values such as:
• 'PK': Primary Key
• 'AK': Alternate Key
• 'FK': Foreign Key
• 'CK': Check Condition
• 'NN': Mandatory Attribute (non-null)

 

8. How are errors tracked in the E$ table?

• Errors are tracked by storing detailed information about the violated constraints and the rejected records in the E$ table. This includes error messages, constraint types, and relevant metadata (such as the check date and origin of the operation).

 

9. Can the error table contain other data besides the columns of the checked datastore?

• No, the E$ error table primarily contains the columns of the checked datastore along with additional metadata about the validation process (such as the error type, message, and constraint information).

 

10. How can the information in the E$ error table be used?

• The information in the E$ error table can be used to identify and address issues in the data. It provides insight into which specific data violations occurred, which constraints were violated, and when the issue happened, making it easier to troubleshoot and fix data quality problems.