Outsource ETL Testing Services
Companies are increasingly focusing on gathering and organizing data for strategic decision-making. The ability to analyze historical trends and monitor operational data in near real-time mode has become one of the key competitive advantages.
The data warehouse plays a very important role when it comes to ensuring the quality of information for analytics. The advanced analytics, in their predictive and prescriptive modes, can go far in the business, reaching a level of knowledge that cannot be achieved in any other way and drive to achieve optimization while minimizing risk; however, the attributes of data quality must be guaranteed. Submitting the data warehouse to tests is the most effective way to achieve it.
There are different types of tests that can be applied to the data warehouse and databases when it is necessary to guarantee that the predictive analytics processes are conducted in terms of total quality. Some of the most interesting are:
-Unit tests: they consist of validating each of the components of a solution, although this type of test must be carried out during the development stage, never afterwards. The most critical elements that must undergo this type of test are, at least, the ETL logic , business rules and calculations implemented in the OLAP layer and the KPI logic . This type of testing is done several times throughout the course of a project and can be automated.
-Tests of the integration system : it depends on the success obtained in the unit tests and must achieve two main goals :
-to Ensure that it can be built and deployed successfully: for which it is necessary to perform system accumulation tests
-Ensure that no problems arise during the execution of the work: with this objective, once implemented and configured , all jobs must be executed and the data processed.
-The adoption of this type of tests in the development cycle of the data warehouse and databases is a giant step forward , which serves to confirm that the system acts in the expected way once the constituent parts of the solution are put together .
-Data validation tests : through this process the data is tested within a data warehouse . A common way to perform this test is through the use of an ad hoc query tool ( Excel) to retrieve data in a format similar to existing operational reports . When the existence of a link between the data warehouse and the operational report is detected , it is shown that the data is valid (unless , of course, the original report be defective ). This test has to be carried out by a representative of the business, since this profile is the one who knows the data better and can validate them with greater guarantees of success.
-User acceptance tests : 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.
-Performance tests : are concerned with properly validating the performance of the solution in real working conditions. To this end, the testing must consider factors such as architecture data, the hardware configuration, system scalability or complexity of consultations .
-Regression tests : this type of test is the process of retesting the functionality to ensure that the development of the data warehouse and databases has not caused any damage to other functionalities and applications. Each of the different categories of tests defined above must be subject to regression testing .
The benefits of the automation of data integration tests
Are you still doing manual integration tests for development projects in your company? The truth is that this approach to testing is still a common practice in many organizations despite the disadvantages involved, among which should be cited the following:
-It is associated with a cost increase associated with damage control , something inevitable when problems are detected late.
-Many times the lack of automation is the main cause of delays in projects.
-Limit the scale of integration tests that can be performed.
-The limitations in the test lead to a low quality of the data.
-Quality problems are reflected in poorly informed decision making that leads to loss of confidence in the data.
-Therefore, those who care about applying the best practices for data integration tests do not hesitate to opt for automation , which allows them to:
-Define your test data sets to perform more complete tests.
-Take advantage of masked subsets of production data or synthetic test data to satisfy your test requirements.
-Perform comprehensive and exhaustive tests of all data integration processes, regardless of the scale of their environment.
-Complete the integration tests quickly and efficiently.
-Reuse and customize the test rules and facilitate the review of the results in the control panel.
-When the data integration tests are carried out based on automated capabilities, it is possible to give a boost to the agility of this type of processes. One of the reasons is that the reuse, auditability and scalability of the tests are supported, and this has a direct impact on productivity.
It’s not just a matter of performance. Also the organization that opts for the automated integration tests benefits from a reduction of the general costs, at the same time that it achieves a better protection of its data and a greater adjustment to the compliance requirements established by its industry.
Do you want to minimize the risks associated with the discovery of errors in production? Are you interested in increasing the reliability of business data? Do you know how to prevent delays in your integration initiatives?