Compare Table Data enhancement request: Add WHERE to compare subset of the data

[Ameen]

Hello
It is really a time consuming when we want to compare just 1 table between 2 schemas, and these tables are too large and we want to figure out the differences in a subset of data only, as we know in advance where the differences could be.
By applying WHERE clause to filter the dataset we want to compare between the 2 tables:
- adding WHERE1 to filter the table in schema1
- adding WHERE2 to filter the table in schema2

It would be a nice addition too if we have an option (yes/no) if we select more than 1 table and we want to apply the same WHERE (WHERE1 and WHERE2) to all selected tables, because sometimes we have many tables that share the same column names and the same WHERE clause is applied to all of them.

To make this easy to write the where clause, use alias for the table in schema1 as T1 (or t1), and the alias for the table in schema2 as T2 (or t2), this applies to every selected table (if we have selected more than 1 table).

Thank you.

 

[Marco Kalter]

I have added this to the list of enhancement requests.

 

[Jim Gust]

Additional enhancement in this area:

1. Ability to exclude specified columns from evaluation
The table I would like to evaluate in each of our environments has a sequence, update_user, and update_dttm. Each database is allowed to be different for those three fields because it is the other columns which matter. Adding this ability would imply that you allow the user to specify a unique key of fields since the natural pk might have been excluded.

2. Open up the compare for views/materialized views
Same as item 1 this functionality implies the user would need to let you know what fields make up the key
Return an error back to the user if the key they specified is not unique, having count > 1

3. Generate a report/spreadsheet/html that allows you to view the differences
In one compare I have 9k differences. I would like to review them in a meaningful way with other departments so that I can determine root cause and make the necessary workflow changes before cleaning up the data.