Maintaining data models
Last updated
Last updated
As building models can be an overwhelming endeavour, it is important to take things one step at a time. Iterating on your models will help you mitigate some of this complexity by slicing a big work package into smaller and simpler tasks
Start by creating the minimum models you will need. Select the bare minimum number of columns you need and start from there.
When you need to add a new information to a model, you should always ask yourself the following question : Should I extend my existing model or create a new one. If you sense that adding a new column will change the purpose of your current models, you should probably create a new one. If not, you should update your model.
Removing columns may be a tricky operation, as your model columns might be used in other part of the BI (explorations, relationships, drills and even other models). If you need to replace a column, we advise you start by creating a new column, update your model and it's dependencies and then remove the column. This should avoid some downtimes and mitigate risks to break downstream items.
Iteration is a never ending process, so don't be afraid to iterate every time it's needed
Building models in Whaly is a great way to quickly deliverer value and iterate on feedback. When some of your Whaly models are widely adopted and become a central part of your BI analysis, you should think about moving them to a more stable and testable environment, such as .
Some core functions of dbt are building, testing and documenting SQL models, as well as providing a complete version control system.
Whaly offers a direct integration with dbt cloud: you will be able to automatically import your dbt models in your Whaly environment in order to use them in your explorations and your charts.