Hey there! Whether you're a seasoned pro or just dipping your toes into the world of data analytics, we all know there's a new player in town —Microsoft Fabric. Since the first Fabric Conference to the most recent updates, a lot of advanced capabilities have been introduced in the game. Let's take a moment to take a step back and brush up our Fabric basics, and ensure you are ready to revolutionize your data game.

Data plays an important role in decision making, and organizations that can harness its power can gain a competitive edge in the market. However, data alone is not enough. To extract value from data, organizations need to build end-to-end data analysis pipelines that can handle the entire data lifecycle, from data ingestion, data cleaning, data transformation, to data visualization and analysis. In this blog post, we will explore why end-to-end data analysis is important for organizations and how it can help them achieve their goals.

In Power Query, there are two functions to manage columns in a table, which are accessible through three different actions in the User Interface. Located on the Home tab, these actions allow you to perform the following operations:
👉 Choose Columns (which calls: Table.SelectColumns)
👉 Remove Columns (calls: Table.RemoveColumns) and
👉 Remove Other Columns (calls: Table.SelectColumns)

Choosing the right method depends on your specific needs. Let’s explore this with a practical scenario. Imagine you have an "Items" table from a small database. Most of your transformations are handled within a Dataflow using Power Query Online to prepare that table for easy use within various semantic models.

In Dataflows, you can opt for removing columns. This allows new columns "added" or made available at the source, to appear without needing to update the Dataflow manually, ensuring flexibility and reducing maintenance.

In semantic Models, you can opt for selecting columns early by choosing only necessary columns from the Dataflow "Items" table for that specific semantic model in development. This locks or limits columns, preventing unneeded columns from bloating the model.

To enhance reliability, make sure to pass an argument for the optional MissingField parameter, this enables you to specify how absent fields should be handled. For example:

• In Dataflows, use MissingField.Ignore to avoid errors when removing non-existent columns.
• In Models, use MissingField.UseNull to maintain a table's structure and allow investigation or reporting of completely empty columns.

Choosing the right method ensures your queries are more robust and reliable, while your models stay lean and manageable.

Learn more about the MissingField.Type