Creating and running detailed test cases to ensure that data in a warehouse is trustworthy, accurate, and compatible with the organization’s data structure is known as data warehouse testing. Because of the rising emphasis on data analytics and how complicated business insights are recognised on the assumption that data is reliable, this procedure is critical for companies implementing and maintaining a data warehouse. 

Data warehouse testing is not simply carried out once data from various sources has been loaded into the warehouse. Rather, it targets the entire data pipeline during extract, transform, and load (ETL) processes when data is in flight. It is possible to rapidly discover and address issue areas by evaluating data at intermediate stages. 

Business intelligence (BI) reports and dashboards that use the consolidated data as their source will also be tested. After all, ETL procedures have been completed, this additional layer of validation certifies the quality of the data. 

Types of Data Testing 

Manual Test Data Generation

In this method, testers manually insert test data under the test case criteria. It is a time-consuming process that is also prone to mistakes. 

Automated Data Testing

Data creation tools are used to do this. The key benefit of this method is its speed and precision. However, it is more expensive than manually generating test data. (“What are the Methods of Test Data? | FlashMob Computing”) 

Back-end Data Injection Testing

SQL queries are used to inject data into the backend. This method can also be used to update data in the database. “It is quick and efficient, but it must be implemented with care to avoid corrupting the existing database.” (“What are the Methods of Test Data? | FlashMob Computing”) 

Using Third-Party Solutions

There are tools on the market that can analyse your test scenarios and then generate or inject data to provide comprehensive coverage. These tools are precise since they are tailored to the company’s demands. However, they are rather pricey. 

Challenges

• There might be an insufficient flow of business information. 
• Data loss may occur during the ETL procedure. 
• The existence of a large amount of data from uncertain source systems 
• SQL programming is required for data warehouse/ETL testing. Data testing is difficult for testers with inadequate SQL coding knowledge. 
• Checking for data correctness in altered columns is difficult. 
• Some of the testing methods are time-consuming. 

Source : https://www.datagaps.com/data-testing-concepts/data-warehouse-testing/ 

Mohammad Saify