Give every dataflow a reasonable name and description.In line with the Microsoft best practices, you can split data ingestion from transformation. If you can, take advantage of linked and computed entities.By doing this, you keep it all well organized and consistent in one place.

Avoid ending up with one big mess! Push all your transformations down to dataflows and avoid adding any logic in the data model.

This all results in a higher success rate on your data model refresh. Is one of your dataflows failing to refresh, it will still contain the last successful set of data and not affect the data model refresh directly.

Start every new solution by using dataflows from the beginning! By leveraging dataflows, you can take advantage of separate refresh schedules and easier error traceability.

I’m definitely not going to repeat everything that is already written down in the docs, but let’s point out a few of the things I always advise to others. In the below video Patrick Leblanc highlights some more advantages of building queries in Power BI desktop and then move them to dataflows.īesides all these personal best practices, Microsoft has also put a bunch of best practices together. Afterwards you can easily copy-paste the query from the advanced editor into a dataflow. Although there was a great improvement of the user interface to build dataflows, I personally still prefer building the queries in Power BI desktop. Dataflow best practicesīuilding dataflows is very similar to building queries in Power BI Desktop. If you are not familiar yet with dataflows, I advise you to first read this documentation before you continue reading this blog. Image coming from the Microsoft documentation about dataflows. Overview of what dataflows are and how they are positioned in the Power BI environment. This lowers the impact on the source by extracting the data once from source to Power BI, helps in centralizing logic, having one version of the truth and lots of other advantages. While having dataflows, you can push down logic and reuse across different datasets. I will describe a few tips and tricks I am applying to sanitize your dataflow approach, organize dataflows and easy to browse through.ĭataflows are increasingly used as shared resource or staging layer inside the Power BI platform.

Handle uncertainty Uncertainty in operations’ time and outcome can be easily represented in simulation models, which allows you to measure risk and find more robust solutions.In this blog I describe a few of the challenges you might face when you have a lot of dataflows.

Increased accuracy A simulation model can capture much more details than an analytical model, which provides for increased accuracy and more precise forecast.

E.g., you can check warehouse storage space utilization at any given date.

Insight into dynamics Unlike spreadsheet- or solver-based analytics, simulation modeling allows observation of system behavior over time at any level of detail.

Visualization Simulation models can be animated in 2D/3D, allowing concepts and ideas to be more easily verified, communicated, and understood.

Save money and time Virtual experiments with simulation models are less expensive and take less time than experiments with real assets.Make the right decision before making real-world changes. Risk-free environment Simulation modeling provides a safe way to test and explore different “what-if” scenarios.

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