Creating a Power BI Data Warehouse workflow

A lightweight data mart in Azure Blob Storage can be used to store historical iMIS data and integrate with Power BI for reporting.

Note that the following concepts are not covered:

• Data warehousing concepts like cubes, dimensions, or facts
• Official Data Warehousing solutions, such as Azure SQL Data Warehouse, Azure Data Lake / OneLake, AWS Redshift, etc.
• Detailed Power BI queries or other reporting techniques for visualizing historical data

Prerequisites

In order to create a Power BI workflow, ensure you have the following:

1. Microsoft Power Automate
   • The iMIS Power Automate Connector
2. Microsoft Power BI
3. A Microsoft Azure Storage Account (with blob access)

Setup

The process is as follows:

1. Creating queries to expose data
2. Creating a scheduled workflow
3. Creating the report

Creating IQAs to Expose Data

Go to RiSE > Intelligent Query Architect, and create as many queries as needed to expose the data. See Building a new query for basic query creation.

In the sample below, there are two datasets :

• Count of contacts by member type
• Order total by the order category

Creating a Scheduled Workflow

To create a scheduled workflow in iMIS Power Automate Connector, you must define when and how often the data needs to be captured.

Review the basic outline of the workflow:

1. (Trigger) On a schedule
2. Execute an IQA
3. Initialize a variable (Type: array)
4. For each record in the query results:
   • Append a JSON object to the array variable
5. Create a blob in Azure Storage with the path: <data category>/<current date>.json (e.g. memberCounts/2023-05-12.json)

Creating the Report

Do the following to create the report in Power BI:

1. Go to Power BI.
2. Click Get Data.
3. In the data source selection window, under Azure, select Azure Blob Storage.

4. Authenticate using the preferred method.
5. On the Navigator screen, select the blob container that contains the JSON data files:

6. Click Transform Data. A list of files appears, assuming that you've run the Power Automate flow at least once.

   📘

   Note

   Power BI can extract the contents of the files themselves, not just the list of files.

7. Find the Content column, right-click, and select Transform > JSON. You'll notice that the column has changed from Binary to Record.

8. Use Power BI to expand this column and incorporate the data into the dataset.

   1. Click the <> icon alongside the Content column, and click OK on the pop-up to include all properties.
   2. Depending on how structure of the data, you'll see some additional columns. If using a nested array, you'll see an object of type List. This list can be expanded so that, instead of one row representing one file, a row can represent one item in the list.

   

   3. Click the <> icon alongside the List column, and select Expand to new rows.

   

   4. Next,use Power BI to expand this Record column into multiple individual columns.
   5. Click the <> icon again and click OK.

   

   The data is now in a usable format.

9. Proceed to rename or delete columns, change their type, or otherwise perform any other necessary M query operations on the data.

10. Click Close & Apply to begin reporting.