Monitor Azure Data Factory Activities with Power BI

Trying to find the number of rows loaded by each activity in Azure Data Factory? Or maybe you are trying to identify which pipelines need enhancements? Or maybe how to send an email with the status of the data load? This blog post will help you monitor Azure Data Factory Activities with Power BI.

A few days ago, I got a request to help notify the number of rows loaded after executing an Azure Data Factory activity. I was also asked to monitor the performance. 

You don’t need to create custom logs activities. Instead, simply enable Azure Log Analytics and create a Power BI report if you want to beautify the data that you want to send. 

Enable Azure Log Analytics for Azure Data Factory 

First, find the diagnostic settings in your Azure Data Factory. 

 find the diagnostic settings in your Azure Data Factory

There are many log options. To monitor activities, enable activity runs only. 

Azure Data Factory Activity Logs help you capture: 

  • The duration of the activities 
  • Azure Data Factory Activity Rows Read and Rows Copied 
  • ADF Data Read and Data Written 
  • ADF Activity Throughput  
  • And much more! 

Note: switching on all the options creates an increase in the ongoing cost of your Azure Log Analytics.  

Azure Data Factory Activity logs

As you can see above, it’s also possible to send logs to storage accounts and event hubs. 

Finally, don’t forget to save the options. 

Monitor Azure Data Factory Activities 

Next, you need to find the Logs option in Azure Data Factory to monitor ADF Activities with Power BI. 

Azure Log Analytics uses Kusto as the coding language, which is case sensitive. 

The following query helps you capture the following information (customize it to meet your requirements): 

  • Subscription ID 
  • Azure Data Factory Name (Resource ID) 
  • Pipeline name and run ID 
  • Activity name, type and run ID 
  • Start and end date of the activity 
  • Duration of the activity, and copy operation 
  • Rows read and copied 
  • Data read and written 
  • Throughput 
  • Data Integration Units used 
  • Parallel Copied used 

The query is filtered to only capture the past 30 days (last line) 

ADFActivityRun  

| where Status =="Succeeded"  // Activity has finished 

| project  

_SubscriptionId,  

_ResourceId,  

PipelineName, 

PipelineRunId, 

ActivityName,  

ActivityRunId,  

ActivityType,  

Start, 

End, 

DurationActivitySec = datetime_diff('second',End,Start), 

DataReadMBs=parse_json(Output).dataRead / 1024 /1024,  

DataWrittenMBs=parse_json(Output).dataWritten / 1024 /1024, 

RowsRead = parse_json(Output).rowsRead, 

RowsCopied = parse_json(Output).rowsCopied,  

ThroughputKBps = parse_json(Output).throughput, 

CopyDurationSec = parse_json(Output).copyDuration, 

UsedDataIntegrationUnits = parse_json(Output).usedDataIntegrationUnits, 

UsedParallelCopies = parse_json(Output).usedParallelCopies 

| where Start > ago(30d) // How back to go in time

You can save the query and pin it to the dashboard. You can also use it in a workbook as described in this blog post about Log Analytics Monitor in Azure Data Factory

Monitor Azure Data Factory Activities with Power BI 

Now it’s time to import the data into Power BI Click the Export to Power BI option. 

import the data into Power BI Click the Export to Power BI option.

A file with the Power BI Query Code will download. 

A file with the Power BI Query Code will download

In Power BI Desktop, click Get Data and a Blank Query. 

Click Advanced Editor. 

In the editor, copy and paste the query from the file to monitor Azure Data Factory activities. 

You are ready to create a Power BI report with visualizations. Example report: 

Example Power BI Report

If you want to send emails, publish the report to Power BI Service. 

Send an email with the Summary of Your Azure Data Factory Activities 

In Power BI Service, open the report and click Subscribe. 

The alert will be displayed as part of the list. 

Summary 

To conclude, today you learned how to further customize how you monitor Azure Data Factory activities. This customization will help you identify performance bottlenecks, points for improvement and send custom Power BI summaries.  

By using this approach, you don’t need to build additional capabilities to cover this functionality. 

When you receive an email, you’ll receive a copy like this: 

Email example

What’s Next? 

In upcoming blog posts, we’ll continue to explore some of the features within Azure Services.       

Please follow Tech Talk Corner on Twitter for blog updates, virtual presentations, and more!     

As always, please leave any comments or questions below.     

CHECK OUT THESE OTHER BLOG POSTS 

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