Monitoring Azure Data Factory v2 Using Power BI

In our previous Power BI automation blog, we utilized the Power Platform in combination with Azure SQL to store refresh history of our datasets leveraging the Power BI REST API when on a Power BI shared capacity. A solution to manage Power BI dataset refreshes is particularly useful when data source refresh times are variable and end users need to know when dataset refreshes are successful. However, Power Automate and Azure SQL create two failure points. This solution also requires additional licensing cost in Power Automate to leverage premium data connectors to Azure SQL. So, how can we consolidate the number of tools we use and at the same time leverage the security features and access controls that Azure provides?

A company performing financial performance analytics year over year on consumer data may run into the challenge of gaining trust regarding the metrics from internal stakeholders. A common pain-point can be contributed to holidays (and their associated sale campaigns) not lining up each year. A reoccurring criticism we have seen at BlueGranite is summed up with the following real-life scenario: “I don’t trust these numbers because Easter was on a different week last year and our stores sell a lot of Easter chocolates.” In this post, we\'ll walk through how to build a comparative system to aid in this instance.

With Power BI Premium and the Capacity Metrics App, you can track metadata such as refresh statistics of your reports and data sources, as well as additional detail regarding CPU usage, memory usage, and average wait times for a dataset to refresh. Refresh data can be invaluable for users monitoring the health of their workspace and troubleshooting issues as well as IT leaders ensuring proper SLA standards from ETL to the Power BI Report. It also provides transparency and trust to business users to know that the data they are viewing is up to date.