Building An End-to-end Data Warehouse Testing Strategy

When it comes to the internet and business technology, we are all surrounded by data. Especially, the introduction of cloud technology has made every small, medium, and large organization rely heavily on data for leading their operations. Though it has added great convenience to the business process, the only thing that bothers most cloud solution service providers is the need for an extensive security and access management.   

More importantly, the process demands cloud testing services or a reliable quality assurance testing services provider who can aid the entire idea of securing the data. This is why most organizations are taking active participation in the end-to-end testing strategies. Most of the time, these strategies include planning, designing, and precise execution of the test cases. Also, a strategic approach to data warehouse testing could even help the testers and developers to meet the process parameters like data inconsistency, ETL process, operational flow, system accuracy, and data security.   With years of expertise in handling data warehouse and cloud testing solutions, we bring you a detailed guide on building an end-to-end data warehouse testing strategy highlighting the need for planning, documenting, and defining the testing goals for the data warehouse.   

Let’s begin!  

Why Do You Need To Plan For Data Warehouse Testing?  

As we have said earlier, data warehouse testing has become a crucial subject for any organization working with cloud technology. Compared to the initial stage of introduction, data warehouse projects have turned to be much more complex, making it more necessary to inject user testing services into the implementation process to make things easier.  

Data warehouse testing allows filtering the data loading from diverse sources to ensure a richer and better assemblage of information could be done. Also, the testing environment makes the data load in a user acceptance testing environment which allows easier deployment of regression, performance, and acceptance.   

The regression testing allows keeping the functionality intact for every new release of ETL code while performance, load, and scalability tests can be run simultaneously to check the scalability of technical architecture while ensuring queries perform within expected periods. Furthermore, the testers from the best software testing company could work on acceptance testing to verify the completeness of the data model while meeting the reporting goals.   

Besides, the data warehouse testing helps testers to review table designs for data validation while giving a review of periodic data to the users with simplified application reports.   

Learning The Data Warehouse Testing Process  

Only a few organizations discard the existing database for any changes or additions made to the existing applications. It means most organizations need to own a reliable database model that can help with data mapping for all the past operations as well as any changes integrated into the applications. However, the only thing that determines the success of the Datawarehouse testing strategy is the effort given to documentation and planning.   

Defining Requirements  

When we say requirements, it is meant to describe the data that must be available in the warehouse, defining how it is organized and updated. However, this step needs the business users and technical teams to work proactively on measuring the requirements that can affect the decision of the implementation.   

Also, the idea of defining requirements needs testers to underline well-established and properly maintained preconditions. This is why testers usually work on the business requirements by assessing the source data requirements, data quality requirements, compliance, infrastructure needs, performance goals, and anything that might affect the data warehouse, either a cloud-based system or BI application.  

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Mapping The Documents  

When it comes to BI solutions or any cloud-based application, it is only accurate mapping of the system dimensions that helps testers and developers to locate each data source and the corresponding transitions.   

It is necessary that any efforts made in the direction of source-to-target mapping should be made with a careful check on all the column names for each source table. The process even involves filtering all the conditions and transformation rules applicable to the ETL processes, the destination columns in the warehouse, and definitions used in the repository. Such an approach not only helps to locate the test parameters but aids in creating a testing strategy focused on customized elements in the system.   

On the other hand, it is very important that businesses should do thorough yet careful verification of the quality of data throughout the warehouse. Failing on the verification part could make any software testing company fall behind on the decisions of filtering the data while diminishing the confidence of the test teams and business users with the use of BI tools as well as any cloud-based application that works on the data warehouse control limiting the QA software testing services to restricted validation and use of conventional tools.   

This adds to the testing benefits like:  

• Resisting loading of bad data  

• Improved validation of financial information  

• Remediation of problems causing data quality issues  

• Advanced documenting of quality management measures, & so on.  

Yield QA Goals With Data Warehouse Testing  

The Process  

The business organizations that are well-informed of the data warehouse testing use it as a tool to simplify the entire testing process giving space for validation at every stage. Also, the process helps to dive into the areas that need maximum focus and must work on a well-defined strategy to achieve the targeted goals related to data validation and integration. Therefore, the entire process of aligning with QA goals through data warehouse testing falls into its place in three major steps:  

• Data Validation: Data validations aim to review the ETP mapping and explore the data samples of the data loaded in the system within the test environment.   

• Data Integration: The next step involves reviewing and accepting the logical data model captured with a data modeling tool that converts the models to database tables within the test environment. Also, the process aids in improved indexing, documenting the metadata, and testing the ETL programs created using the ETL tool or stored procedures.  

• System Testing: The last stage of the data warehouse testing model aims at the increased volume of test data loaded within the system while estimating and measuring the load time and resisting errors with the placing of data.  

The Data Models  

Data warehouse models are an important factor that defines the success of data warehouses as the incorrect and non-existent models could make the entire testing effort lose its credibility. Therefore, the project leaders can take the necessary time to develop the data warehouse models.  

In most cases, the testers need to work on multiple building efforts using an experienced data warehouse modeler to align with the comprehensive business requirements. The process even needs a data warehouse modeler to build the model, making warehouse modeling an important skill to practice for the entire warehouse team.   

The software testing company uses the data architecture and model as the blueprint of the warehouse to project the bigger picture of the data warehouse implementation to understand the right path to deployment. Also, data models aid testers and developers in establishing key relationships between critical data sources and any major system components that can affect the deployment and use.  

Since requirements are the heart of data warehouse testing, testers and developers taking combined efforts on measuring the data warehouse requirements could ensure easier validation of the data warehouse models. Meanwhile, testing allows entire technical teams to locate errors in the mapping of the database while adding to the implementation and curbing the costs that incur due to bad test data.  

The Crux  

To conclude it all, defining the best approach to data warehouse testing demands extensive focus and time on understanding the QA benchmarks associated with the data warehouse. A relevant test plan not only aids in achieving the scope of the testing strategy but improves the overall results with the testing of project software and data. Besides, your QA software testing services should also help you to target the resources, tools, and overall outlining of the test approach in context to project deliverables.  

More importantly, data warehouse testing allows you to leverage data for a wider range of applications which improves the overall business decisions and helps attain the bottom line early.   

And if you are struggling with your data warehouse solution that is bringing your unnecessary errors and complications of use, consider taking expert assistance from our ISTQB certified testers at BugRaptors. Schedule A Free Consultation Call Today!