Enable fact-based decision making by maximising access to data and information
Data Warehouse TestingMaximise Access to Data. One of the most important assets of any organisation is its information. Data Warehouse is a collection of the raw or business information that is designed for query and analysis rather than transaction processing.
The volume of data continues to grow as we populate the warehouses with increasingly atomic data and update them with greater frequency.
More importantly, armed with access to the data warehouses, business professionals are making better decisions and generating payback on their data warehouse investments.
The success of any Data Warehouse (DWH) solution lies in its ability to not only analyze huge amounts of data over time but also to provide stakeholders and end-users meaningful options that are based on real-time data.
While details are elaborated below, it is essential that a good DWH test strategy should cover validation of loading of all required rows, correct execution of all transformations and successful completion of the cleansing operation. The team also needs to thoroughly test SQL queries, stored procedures or queries that produce aggregate or summary tables. Keeping in tune with emerging trends, it is also important for test teams to design and execute a set of test cases that are focused on customer experience.
The focus of Data Warehouse test strategy is primarily on four key aspects
- Data quality validation
- End user & BI / report testing
- Load and performance testing
- End-to-End (E2E) regression and integration testing
End to end Integration Testing on Data warehouses and all their intervening components
Facilitate improvements in specific focus areas as well as outline long term strategic road maps
Process-driven and tested frameworks along with our experience in recommending the best combination of tools and technologies
Key Aspects of Data Warehouse Test Strategy
Data quality validation
This is core to any data warehouse tests and includes tests for data completeness, data transformation and data quality.
Data completeness tests
Designed to verify if all the expected data loads into the data warehouse, ensuring that all records are completely loaded without errors in content quality or quantity.
Data transformation tests
Designed to verify the accuracy of the transformation logic or transformation business rules.
Data quality tests
Designed to validate system behavior when data is rejected (example: data inaccuracy or missing data) during data correction and substitution. Scenario-based tests and validation tests for the solutions’ reporting feature are part of this test.