Testdata gets processed by different Test-Algorithms to check quality and plausibilize information.
BiG EVAL allows to compare scalar values (e.g. KPI's) to values originating from other data sources completely technology neutral.
This technique allows many different kind of data testing:
- Compare row-counts between source systems and the data warehouse.
- Compare data with reference- or source-data.
- Check wether data was copied or transformed right.
- Calculate back KPI's and compare intermediate results to source data.
- and more.
Fuzzy comparisons are used for data validation and plausibilization. They allow you to define tolerance values between actual and reference data.
- Check wether the revenue of the actual month compared to the previous monts is within a valid range.
- Compare relation between KPI's to plausibilize them.
Test the performance of your data systems against the users requirements and expectations. Especially of reporting and analysis systems.
The schema drift testmethod monitors the schema of a database and fails when there are changes (new or changed objects like tables, columns, datatypes etc).
This testmethod gives a lot of value if there is a lack in communication between teams that are responsible for data source systems and the data warehouse. Usually the ETL process fails if there are any unknown changes in a source system. The schema drift testmethod alerts the data warehouse team when there are changes.
BiG EVAL is capable of comparing table-structed data from one system with data of another system. This allows you to find data that was copied or transformed incorrectly.
- Compare attributes of dimensions with the source data.
- Find differences.
- Check wether all businesskeys from a source system are available in the data warehouse or analysis system.
- and many more.
If the out-of-the-box provided functionallity of BiG EVAL isn't enough to fulfil your requirements, you can use the scripting capabilities of BiG EVAL to implement your own test algorithms.
Using the Rules test-method BiG EVAL checks wether data records match your criterias for a high-quality information or not.
Defining criterias is done by a simple epression-syntax. Expressions are extendable by a C#-Script to fulfil the most complex requirements as well.
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