In this era of data-driven decision making, data quality holds immense significance. Accurate data has been gauged to be a priority factor in customer experience enhancement and creating a business strategy. For reliable data that a company can trust, it is imperative to test data warehouse architecture and implementation.
Why Is Data Warehouse Testing Important?
To set up the best in class agile data warehouse environment, you need to focus on its foundation: the development of a robust testing strategy and tools. As a strategic enterprise resource, there is a heavy reliance on a data warehouse to be trustworthy, well-governed, and appropriately valued. A data warehouse without these attributes is susceptible to data quality challenges and information hiccups. Testing is required to minimize, and hopefully avert,the occurrence of such challenges. Agile development offers more opportunities for testing than traditional projects.
Additionally, given that most agile data warehouses employ ETL processes, they require accurate and comprehensive testing, as well as QA processes specifically applied to ETL. The quality of the colossal amount of source data from different data repositories cannot simply be assumed. It needs to be profiled and cleaned up to remove any inconsistencies or redundancies. Testing helps ensure that all the data behaves as expected at the extraction, transformation, and loading phases.
Effective testing will,therefore, help you uncover problems with data quality, data inconsistency,data security, performance, ETL process, and the end user experience. It’s vital that your data warehouse solution incorporate accurate testing mechanisms.
How to Build an End-to-End ETL DW Testing Strategy
End-to-End testing serves the purpose of validating the data warehouse and integrating the system seamlessly with external interfaces.Additionally, it validates data processing from other upstream and downstream systems.
The end-to-end ETL process should be the primary target of testing. Towards this end, the significant steps include correctly executing all transformations, validating loading of all required rows, successfully completing cleansing, and comprehensively testing SQL queries and stored procedures or queries. Since outstanding customer engagement is the primary goal, the test team needs to ensure its tests have been designed and executed to reflect the customer’s experience and perspective correctly.
End-to-end testing begins by identifying source data. The identified and extracted source data is continually tested using initial and incremental loads throughout the process. The data goes from source extract to staging. The cleansed dimensional data then reaches the operational data store, and the fact data to the data warehouse. The report and portal functions extract the data to display and report it.
Core best practices for end-to-end data warehousing test strategy include beginning the formal QA data track verification early in the ETL design process and data load stage,and continuing them throughout deployment and production. Early access should be given to testers so they can evaluate initial data load quality, identify issues early, and provide valuable feedback to development teams before they start formal testing.
Writing an effective data warehouse test plan is a foundational aspect of the testing effort. This plan allows test engineers to verify and validate the data requirements from the source to target, i.e. end-to-end. These data requirements are stated in the business requirement documents, source to target mappings, ETL design documents, and data models for the source and target schemas. The test plan will explain the QA staff’s techniques to ensure that the data warehouse fulfills the requirements. A well-made test plan, therefore, serves as a contract of sorts between the other project stakeholders and the QA team.
End-to-end testing strategy also needs to consider factors like the project schedule,incident and error handling system, configuration management system, data quality verification process, test objectives, tasks, tools and deliverables, QA staff training needs and resources estimates, QA roles and responsibilities, testing environment budget and plan, and defect reporting requirements. Additionally,it needs to consider the entrance criteria that must be met before formal testing begins and the exit criteria that have to be met before the formal testing ends.
The Data Warehouse Verification Stage
To develop a test strategy, you need to understand data modeling, and map data from source to target. There are multiple sub-stages to verification that are spread throughout the data warehouse implementation lifecycle across different project testing phases. Verification includes procedures such as:
- Data completeness, which ensures all expected data gets loaded through each ETL process. These tests include aggregates (sum, avg, min, max),Compare and Validate counts, and the actual data between the source and target system.
- Data quality, which ensures all invalid data is accurately reported, ignored, rejected or corrected. Data profiling identifies data quality issues, and the ETL is accordingly designed to deal with the issue. The automation of these checks between the source and destination can alleviate problems after implementation.
- Data transformations which ensure all data waiting to be transformed is correctly completed, complying with design specifications and business rules.
- Integration testing, which tests the data end-to-end in the ETL process and application destination, ensuring the ETL procedure functions with other upstream and downstream processes well.
- Performance and scalability, which ensures data load and query performance within expected time frames, and the scalability of technical architecture. While ETL performance testing ensures every step of the ETL process works within expected data volumes, it cannot use real data to imitate the accurate volumes.
- User acceptance testing, which ensures user expectations are met by the data warehousing solution and future expectations are anticipated.
- Regression testing,which ensures the integrality of existing functionality every time a new release of data and ETL code is completed, validating the same output for input before and after the change.
- Metadata testing, which ensures the table definitions match the application design specifications and data model. It includes data length check, index/constraint check, and data type check.
- Incremental ETL testing, which verifies that the updates on the sources are accurately loaded into the destination.
Make sure your data warehouse environment is well-tested before its deployment and integration with different systems powering your organization. Contact our data architects to create the most suitable solution for keeping your data accurate.