In current trend, every software development, enhancement, or maintenance project includes some quality assurance activities. Quality assurance attempts defects prevention by concentrating on the process of producing the rather than working on the defect detection after the product is built. Regression testing means rerunning test cases from existing test suites to build confidence that software changes have no unintended side-effects. Data warehouse obtains the data from a number of operational data source systems which can be relational tables or ERP package, etc. The data from these sources are converted and loaded into data warehouse in suitable form, this process is called Extraction, Transformation and Loading (ETL). In addition to the target database, there will be another data base to store the metadata, called the metadata repository. This data base contains data about data-description of source data, target data and how the source data has been transformed into target data. In data warehouse migration or enhancement projects, data quality checking process includes ensuring all expected data is loaded, data is transformed correctly according to design specifications, comparing record counts between source data loaded to the warehouse and rejected records, validating correct processing of ETL-generated fields such as surrogate keys. The quality check process also involves validating the data types in the warehouse are as specified in the design and/or the data model. In our work, have automated regression testing for ETL activities, which will saves effort and resource while being more accurate and less prone to any issues. Author experimented around 338 Regression test cases, manual testing is taking around 800 hrs so with RTA it will take around 88 hrs which is a reduction of 84%. This paper explains the process of automating the regression suite for data quality testing in data warehouse systems.