Asana
Power BI is a powerful business intelligence tool that allows users to connect to multiple data sources, create dynamic visualizations, and share insights with others in their organization. One of the many data sources that Power BI can connect to is Asana, a project management tool used by teams to track tasks, projects, and workflows. In this article, we will explore how to use Power Query M language code to connect to the Asana data source from inside Power BI.
Overview of Power Query M Language
Power Query is an ETL (extract, transform, load) tool that allows users to connect to various data sources, transform the data, and load it into Power BI. Power Query uses a functional language called M to perform these operations. M is a case-sensitive language that is similar to Excel formulas but with more advanced functionality. M code is generated by Power Query when users perform transformations on data, and users can also edit the code to create their own custom transformations.
Connecting to Asana Data Source in Power BI
To connect to Asana data source in Power BI, we can use the Web connector in Power Query. The Web connector allows users to connect to various web-based data sources, including Asana. Here are the steps to connect to Asana data source in Power Query:
1. Open Power BI and click on the “Get Data” button in the Home tab.
2. In the “Get Data” window, select “Web” as the data source and click on the “Connect” button.
3. In the “From Web” window, enter the Asana API endpoint URL in the “URL” field. The Asana API endpoint URL can be obtained from the Asana developer documentation site.
4. Click on the “OK” button to connect to the Asana data source.
Once connected, Power Query will retrieve the data from Asana and display it in the Power Query Editor window. From here, users can perform various transformations on the data using M language code.
Using Power Query M Language Code for Data Transformation
Power Query M language code can be used to perform various data transformations on the Asana data source. Here are some examples of M language code functions that can be used:
Table.SelectColumns
The Table.SelectColumns function is used to select specific columns from a table. Here’s an example of how to use this function in Power Query M language code to select the “Task Name” and “Due Date” columns from the Asana data source:
= Table.SelectColumns(#”Asana Data”,{“Task Name”, “Due Date”})
Table.RenameColumns
The Table.RenameColumns function is used to rename columns in a table. Here’s an example of how to use this function in Power Query M language code to rename the “Task Name” column to “Task”:
= Table.RenameColumns(#”Selected Columns”,{{“Task Name”, “Task”}})
Table.TransformColumns
The Table.TransformColumns function is used to transform the values in a column. Here’s an example of how to use this function in Power Query M language code to convert the “Due Date” column to a date format:
= Table.TransformColumns(#”Renamed Columns”,{{“Due Date”, each Date.FromText(_), type date}})
Table.Group
The Table.Group function is used to group rows in a table based on a common value. Here’s an example of how to use this function in Power Query M language code to group tasks by their project:
= Table.Group(#”Transformed Columns”, {“Project Name”}, {{“Tasks”, each _, type table [Task Name=text, Due Date=date, Project Name=text]}})
These are just a few examples of the many M language code functions available in Power Query. Users can also create their own custom functions using M language code to perform more complex data transformations.
Conclusion
In conclusion, connecting to the Asana data source from inside Power BI is easy using Power Query and M language code. With the use of various M language code functions, users can transform the data from Asana into meaningful insights and visualizations that can be shared with others in their organization.
