How to Move Data Between Dataiku and Power BI?
There are several ways to move data between Dataiku and Power BI, and the best approach depends on where the data is stored and whether you need to work with the Power BI semantic model itself.
A common architecture is:
Source database → Dataiku → Data preparation/processing → Output dataset → Power BI
If both tools can access the same underlying database, this is often the cleanest approach. Dataiku can be used for data cleansing, transformation, quality checks, and analytics, while Power BI can consume the prepared data for reporting and visualization.
Another simple option is to have Dataiku produce an output file or database table and then connect Power BI to that output. This works well for smaller workflows and is easier to implement than building an API integration.
If the requirement is specifically to retrieve information from a Power BI semantic model, the Power BI REST API is another option. Microsoft provides APIs for working with datasets/semantic models, although the available operations depend on the model type, permissions, and Power BI environment.
For example, the workflow could be:
Power BI semantic model → Power BI API → Dataiku Python/API workflow → Dataiku dataset → analysis
Before choosing an approach, I would first determine whether you actually need to extract data from Power BI or whether you can connect Dataiku directly to the original data source. In many cases, accessing the original source is simpler and gives you more flexibility.
It is also useful to understand Power Query, data modelling, DAX, semantic models, and data pipelines when working across Power BI and Dataiku. For anyone building these skills, practical Power BI training can be useful, particularly when it includes hands-on work with real datasets rather than only dashboard design.
Has anyone implemented a Dataiku → Power BI or Power BI → Dataiku workflow in production? I’d be interested to know which architecture you used and what limitations you encountered.