Post by ummefatihaayat12 on Feb 28, 2024 1:47:25 GMT -5
The data warehouse plays a very important role when it comes to ensuring the quality of information for analytics. Advanced analytics, in its predictive and prescriptive modalities, allows the business to go very far, by achieving a degree of knowledge that cannot be achieved in any other way and by promoting its optimization while minimizing risk; However, data quality attributes must be guaranteed. Testing the data warehouse is the most effective way to achieve this. Data warehouse: the test of quality There are different types of tests that can be applied to the data warehouse and databases when you want to guarantee that predictive analytics processes are conducted in terms of total quality. some of the most interesting are: 1. Unit tests: consist of validating each of the components of a solution, although this type of test must be carried out during the development stage, never after.
The most critical elements that must undergo this type of testing are, at least, the ETL logic , business rules and calculations implemented in the OLAP layer and the KPI logicThis type of testing is performed several times throughout the course of a project and can be automated . Integration system testing : depends on the success obtained in the unit tests and must achieve two main goals : to. Ensure it can be built and deployed successfully – this requires system build-up testing b. Ensure that no problems India Part Time Job Seekers Phone Number List arise during job execution : to this end, once deployed and configured , all jobs must be executed and data processed. Adopting this type of testing in the data warehouse and database development cycle is a giant step forward , serving to confirm that the system performs as expected once the constituent parts of the solution are put together. Data validation testing : through this process, data is tested within a data warehouse . A common way to perform this test is by using an ad hoc query tool ( Excel) that allows data to be retrieved in a format similar to existing operational reports.
When a link between the data warehouse and the operational report is detected, the data is proven to be valid (unless , of course, the original report is faulty ). This test must be carried out by a business representative, since this profile is the one who best knows the data and can validate it with greater guarantees of success . 4. User acceptance testing : its objective is to ensure that the data provided to the end user meets their expectations and that the same happens with the tools that are made available to them. 5. Performance tests : these are responsible for adequately validating the performance of the solution in real working conditions. To do this, in testing you must consider factors such as data architecture, hardware configuration, system scalability or query complexity . 6. Regression testing: This type of testing is the process of retesting the functionality to ensure that the development of the data warehouse and databases has not caused defects in other functionalities and applications. Each of the various categories of tests defined above must be subject to regression testing . Related posts: Data quality and forecasting leaders Data mining models and the most used tools How far does data mining marketing allow you to go?
The most critical elements that must undergo this type of testing are, at least, the ETL logic , business rules and calculations implemented in the OLAP layer and the KPI logicThis type of testing is performed several times throughout the course of a project and can be automated . Integration system testing : depends on the success obtained in the unit tests and must achieve two main goals : to. Ensure it can be built and deployed successfully – this requires system build-up testing b. Ensure that no problems India Part Time Job Seekers Phone Number List arise during job execution : to this end, once deployed and configured , all jobs must be executed and data processed. Adopting this type of testing in the data warehouse and database development cycle is a giant step forward , serving to confirm that the system performs as expected once the constituent parts of the solution are put together. Data validation testing : through this process, data is tested within a data warehouse . A common way to perform this test is by using an ad hoc query tool ( Excel) that allows data to be retrieved in a format similar to existing operational reports.
When a link between the data warehouse and the operational report is detected, the data is proven to be valid (unless , of course, the original report is faulty ). This test must be carried out by a business representative, since this profile is the one who best knows the data and can validate it with greater guarantees of success . 4. User acceptance testing : its objective is to ensure that the data provided to the end user meets their expectations and that the same happens with the tools that are made available to them. 5. Performance tests : these are responsible for adequately validating the performance of the solution in real working conditions. To do this, in testing you must consider factors such as data architecture, hardware configuration, system scalability or query complexity . 6. Regression testing: This type of testing is the process of retesting the functionality to ensure that the development of the data warehouse and databases has not caused defects in other functionalities and applications. Each of the various categories of tests defined above must be subject to regression testing . Related posts: Data quality and forecasting leaders Data mining models and the most used tools How far does data mining marketing allow you to go?
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