Introduction to Wrangling Data Flows in Azure Data Factory

Hello! It’s been a while since I’ve done a video on Azure Data Factory. To get back in the flow of blogging on ADF I will be starting with Data Flows, specifically Wrangling Data Flows.

The video can be seen here:

What are Wrangling Data Flows in Azure Data Factory?

Wrangling Data flows are a method of easily cleaning and transforming data at scale. huh?

Wrangling Data Flows uses the M query language and the UI experience provided by the Power Query Editor in Power BI Desktop. This is a brilliant move by Microsoft to include this technology in Azure Data Factory. Just think of the hundreds of millions of people who currently are transforming and cleaning their data in Excel or Power BI Desktop. Now they can take their self service ETL (extract, transform and load) skills to the enterprise level with ADF.

What makes it scalable? Power Query Editor at Scale.

Wrangling data flows allows the developer to use the graphical user interface to do all the hard work with minimal to no code. But in the background all of your UI steps are being converted to the M language. At runtime, Azure Data Factory will take that M code and convert it to Spark and then run your data flow against big data clusters. This means as your data volumes grow, you should experience consistent performance!

Are there any limitations with Wrangling Data Flows?

Yes… quite a few actually. Wrangling Data Flows are still in preview at the time of this blog and the related YouTube video. Currently there are quite a few operations that just aren’t supported. The most obvious of those operations being promoting header rows and pivoting data. I hope that these features will be available once the product is in GA.

As always, thank you for reading my blog and watching my YouTube videos! Have a great day!!

Other Azure Data Factory resources!

Why are Asynchronous components so bad in SSIS?

If you have spent anytime with SSIS you have heard that Asynchronous components also known as “Blocking” components are bad for performance and should be avoided where possible. You have also heard some analogy or another that explained how these components “block” the data in the data flow and slow down performance. But I’m here to tell you that it gets much worse.

The data flow sends data down in buffers. Each buffer in the data flow will first utilize memory if available, the memory that is captured for that buffer of records will be released once the data flow is done with that buffer. For example once those records in that buffer have been loaded to the destination the buffer and the corresponding memory is released. This memory can now be used for the next buffer of memory coming down the pipeline in the data flow task. This is where the true evil with asynchronous components come in.

What happens if the buffer is not released because it got caught up in the data flow at some blocking transform?  In a worse case scenario your machine/server becomes starved for memory and then SSIS is forced use disk instead of memory. Too many buffers spooled to disk can exponentially increase your package run time. Let me demonstrate.

For this example I have created two packages. Each package uses a sorting transform, however in different locations.

Package One. This package takes an unknown amount of time to run, I cut it off after approximately 68 minutes. The first screenshot shows the package in it’s current state, the second and third screenshot give some insight into why this is taking so long to run.

Screenshot 1:


The following screenshot is obtained from SSMS after I run the package from integration services and then run the following query against the SSISDB.

  • “SELECT * FROM [catalog].[dm_execution_performance_counters] (NULL) “

Screenshot 2:


Screenshot 3: (Here we see the execution performance from the SSISDB)


Unfortunately there are times when you just have no choice and must use asynchronous components in SSIS. One option to consider, can the blocking transform be placed in a more strategic location to minimize the negative impact to performance? For example. In package one the sorting transform was placed before the lookup, the lookup is only forwarding non matching records to the destination, so why do the matching records need to be sorted? If matching records don’t need to be sorted then we can move the blocking transform to after the lookup and keep everything in memory.

Package Two: (This package completes in 1 minute and 20 seconds)

Screenshot 1:


Screenshot 2:


Screenshot 3:


Thanks for looking!