In the ETL layer, a data quality check is performed on the input and output datasets. These checks are usually similar no matter the industry or business needs. The goal of data integrity checks is to ensure that the data is accurate and complete. Different row counts in different parts of the dataset indicate the addition or loss of data. Also, a high null count or a duplicate record indicate incorrect data. These types of errors should be corrected in the ETL process.
How to Do Data Quality Check in ETL
The process of data integration testing involves running reference tests and syntax tests to ensure that the data is valid and consistent. The main purpose of data integration testing is to ensure that the data is loaded into the target data warehouse correctly. A data quality check will also check for threshold values and ensure that data is correctly formatted. Report testing checks the information that is presented in summary reports to check for calculations, layout, and functionality. Other types of testing in the ETL process include GUI, user acceptance testing, and application migration tests. It is possible to automate some of these tests and still perform a simple data quality check in Etl.
Once the data has been loaded into the staging area, data quality checks are applied. These checks ensure that the data is unique and accurate, and that it does not duplicate any existing data. These checks can also be performed on the data as it comes from the final destination. This process is critical to ensuring the integrity of data. The data quality checks performed during the ETL pipeline are the most effective in ensuring that data has the right quality.