Rathnadevi Manivannan gives an example of using Drools to create rule-based data cleansing processes:
The oil well drilling datasets contain raw information about wells and their formation details, drill types, and production dates. The Arkansas dataset has 6,040 records and the Oklahoma dataset has 2,559 records.
The raw data contains invalid values such as null, invalid date, invalid drill type, and duplicate well and invalid well information with modified dates.
This raw data from the source is transformed to MS SQL for further filtering and normalization. To download raw data, look at the Reference section.
This is an example of applying several constraints and rules to a single data set. Each individual rule would probably be easier to do in T-SQL, but the whole bunch becomes easier to understand with a procedural language.