Data Vault (DV) is a Data Warehouse modelling approach. Qlik is not a Data Warehouse product and needs a much smaller slice of a Data Warehouse Model to function correctly. This slice also needs to be transformed in some way to be useful. Qlik lends itself best to a dimensional data model approach. DV quote: "The Data Vault Model is a detail oriented, historical tracking and uniquely linked set of normalized tables ....". Qlik performs usually better with a denormalized table-structure with a star-schema or snowflake-schema.
So try searching for how to create a dimensional model out of DV. Mapping a traditional dimensional data mart into a Qlik data model is very easy.
A quote from Wikipedia on Data Vault:
"The data vault modeled layer is normally used to store data. It is not optimized for query performance, nor is it easy to query by the well-known query-tools such as ............ Since these end-user computing tools expect or prefer their data to be contained in a dimensional model, a conversion is usually necessary."
However:
Even though it is best practice, as far as I know and have experienced, to do dimensional modelling in Qlik - the Qlik associative engine makes it much easier than most other tools to have a normalized approach - although with a performance penality. This penality might not matter that much as long as the volume of data in the data model is not too large.
The benefit of the associative engine is that Qlik automatically associate the different tables and as a consequence expressions will calculate as-if your data model is a big denormalized table. This in turn should make it easier to create visualizations and expressions to calculate within them even following a DV approach.
