Datatype Conversion in Power Query Affects Data Modeling in Power BI

Datatype Conversion in Power Query Affects Data Modeling in Power BI

In my consulting experience working with customers using Power BI, many challenges that Power BI developers face are due to negligence to data types. Here are some common challenges that are the direct or indirect results of inappropriate data types and data type conversion:

  • Getting incorrect results while all calculations in your data model are correct.
  • Poor performing data model.
  • Bloated model size.
  • Difficulties in configuring user-defined aggregations (agg awareness).
  • Difficulties in setting up incremental data refresh.
  • Getting blank visuals after the first data refresh in Power BI service.

In this blogpost, I explain the common pitfalls to prevent future challenges that can be time-consuming to identify and fix.


Before we dive into the topic of this blog post, I would like to start with a bit of background. We all know that Power BI is not only a reporting tool. It is indeed a data platform supporting various aspects of business intelligence, data engineering, and data science. There are two languages we must learn to be able to work with Power BI: Power Query (M) and DAX. The purpose of the two languages is quite different. We use Power Query for data transformation and data preparation, while DAX is used for data analysis in the Tabular data model. Here is the point, the two languages in Power BI have different data types.

The most common Power BI development scenarios start with connecting to the data source(s). Power BI supports hundreds of data sources. Most data source connections happen in Power Query (the data preparation layer in a Power BI solution) unless we connect live to a semantic layer such as an SSAS instance or a Power BI dataset. Many supported data sources have their own data types, and some don’t. For instance, SQL Server has its own data types, but CSV doesn’t. When the data source has data types, the mashup engine tries to identify data types to the closest data type available in Power Query. Even though the source system has data types, the data types might not be compatible with Power Query data types. For the data sources that do not support data types, the matchup engine tries to detect the data types based on the sample data loaded into the data preview pane in the Power Query Editor window. But, there is no guarantee that the detected data types are correct. So, it is best practice to validate the detected data types anyway.

Power BI uses the Tabular model data types when it loads the data into the data model. The data types in the data model may or may not be compatible with the data types defined in Power Query. For instance, Power Query has a Binary data type, but the Tabular model does not.

The following table shows Power Query’s datatypes, their representations in the Power Query Editor’s UI, their mapping data types in the data model (DAX), and the internal data types in the xVelocity (Tabular model) engine:

Power Query and DAX (data model) data type mapping
Power Query and DAX (data model) data type mapping

As the above table shows, in Power Query’s UI, Whole Number, Decimal, Fixed Decimal and Percentage are all in type number in the Power Query engine. The type names in the Power BI UI also differ from their equivalents in the xVelocity engine. Let us dig deeper.

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