There have been lots of announcements about Dataflow online, through videos and articles and news. There are many demos about Dataflow as well. However, still when I talk about it in conferences, one of the main questions from the audience is that; “What are use cases for it?”. In this blog post, I’m going to answer that question specifically. I will first explain what Dataflow is, and how it can be useful in different scenarios for business analysts or developers who are using Power BI. If you want to use dataflow in Power BI, but not sure where to use it, this is an article for you. If you want to learn more about Power BI, read Power BI book from Rookie to Rock Star.
What is Dataflow
A simple definition of Dataflow is running Power Query in the cloud. Well, this might not seem very new feature, correct? because right now, you can publish your *.pbix report into Power BI, and then schedule your report to refresh. That means you are scheduling a Power Query script in your report to run in the cloud. So what is new then with Dataflow? The new part is that Dataflow is not part of a single report. So better definition for Dataflow is;
Dataflow is a Power Query process that runs in the cloud independently from any Power BI reports.
Where the Output Stored?
If the Power Query scripts run independently from the report, then where it stores the data? That is a very valid question. Because when Power Query is part of a report, then the output of each query will load into the Power BI model (or let’s say Power BI Dataset). In the case of Dataflow, we said there is no report bind to it, so there is no dataset bind to it then. Dataflow has to store the output of the query somewhere.
Dataflow stores the data in the Azure Data Lake storage.
Azure Data Lake storage is Microsoft cloud storage that can store structured data (like tables) and unstructured data (like files).
But I Don’t Have Azure Data Lake Subscription!
If you don’t have an account in Azure or you don’t have a subscription that you can use for Azure Data Lake, No need to worry! You can still use Dataflow. The whole process of storing data into Azure Data Lake is internally managed through Dataflow. You won’t even need to login to the Azure portal or anywhere else. From your point of view, in the Power BI website, you create a dataflow, and that dataflow manages the whole storage configuration. You won’t need to have any other accounts or pay anything extra or more than what you are paying for Power BI subscription.
Dataflow manages the Data Lake configurations internally. You won’t need anything except your Power BI accounts and subscriptions.
If, however, some users have their own Azure subscriptions and prefer to manage the storage method in the Data Lake themselves. There are methods that they can work with Dataflows and configure their own Azure Data Lake to be the storage engine.
Power BI Can Do Get Data from Dataflow
The other end of the solution with dataflow is that; Power BI has a Get data option from Dataflow. You do not need to connect to Azure Data Lake and find where the data is stored (especially if you are using the built-in storage of dataflow and not the external dataflow with your own data lake storage). You can simply Get Data from a Dataflow in Power BI, and choose the table that you want.
What is the Benefit of Using Dataflow?
We are getting on the right path now. You know what Dataflow is, and now is the time to see what is the usage of it? Why and how running a Power Query script in the cloud independently from a Power BI dataset can be useful? Let’s dig into the answer together through some examples.
Using One Power Query Table in Multiple Power BI Reports
Have you ever had the need to use one Power Query table in multiple Power BI Reports? Of course, you did. If you worked with Power BI for some time, you know that tables generated through Power Query are only part of one Power BI file. If you want to use the same table in another file, with a combination of some other tables which is not in the first file, then you would need to replicate the Power Query transformations (or copy and paste the M script) into the new *.pbix file. You may say, no I don’t, but here is an example: Date Dimension!
Date dimension is a table that you use in a *.pbix let’s say for Sales Analysis, and also in another *.pbix for Inventory reporting, and for HR data analysis *.pbix too. So what do you do in these situations? Copying the script for Date Dimension in all of these three files? What if after a year, you decided to add a transformation or a column to the date dimension? then you need to replicate this change in all *.pbix files that you have, otherwise, your code becomes inconsistent. It would have been much better if you did the transformation once, stored the output somewhere, and then re-use it. That is exactly what Dataflow can do for you!
Re-usable tables or queries across multiple Power BI files, are one of the best candidates for Dataflow.
Different Data Source with Different Schedule of Refresh
What if you have a dataset that includes two tables with totally different schedule options. For example, the Sales transactions table coming from SQL Server database is changing every day, and you need to refresh this data every day. However, the mapping table used for some of the products and maintained by the product team is only changing every quarter. If you have both of these queries in one *.pbix file, then you have no other choice but to refresh at the maximum frequency needed which is once a day.
However, if there be a mechanism that can refresh the mapping table every quarter, apply all transformations needed, and store it in a table. Then on every day you just need to read it. Dataflow can do that for You; With one query running the data transformation script and loading it into a destination table. This can be scheduled for whatever plan you need.
Dataflow can run extract, transformation, and load (ETL) process on a different schedule for every query (or table).
Centralized Data Warehouse
With the evolving of Power BI and other self-service technologies, many companies started to implement a BI system without having a data warehouse. However, if the number of BI systems increases, the need for a centralized data warehouse appears quickly. A data warehouse is a specifically designed database to store data in the format needed for reporting. In traditional BI systems, one of the phases of building a BI system, and one of the most important phases let’s say, is to create a centralized data warehouse. The ETL process will extract data from data sources, and load it into the centralized data warehouse. All reporting solutions, then use the data warehouse as the single source of truth.
Dataflow can be an important part of building a centralized data warehouse for your Power BI solution. You can build the structure you want through Power Query scripts in a dataflow. Dataflow then runs those scripts and stores the data into output tables. Output tables of dataflow can act as a centralized data warehouse for your *.pbix files. Alternatively, you can have your own Azure Data Lake storage and configure it the way that you want, with the structure of tables that you want, and get dataflow to load data into those tables.
Dataflow can be the ETL engine, that fuels the centralized data warehouse in Azure data lake storage.
Versioning Data from a Data Source
One of the most requested features for Power BI users is that, how can I store the output of this report (or in our case a Power Query script) into somewhere, and I can refer to it later? The data is changing every day, but you may want to keep a version of the data at this point of the time and store it somewhere for later use. For example, getting a version of the data at the end of every fiscal year, or period or quarter. Dataflow, although, not built for that purpose, can do that for you. Using dataflow, you can create different dataflow processes to run the ETL anytime you want and store it in a different output table each time.
Dataflow can be used for versioning the data from the source into multiple destination tables.
Other Use Cases
Do you use Dataflow in Power BI for any other use cases? Feel free to share your experience below at comments.
How to Create Dataflow and Use it?
Now that you know what is the Dataflow and benefits of it, Is time to talk about it how to use it. Stay tuned for the next blog posts about Using Dataflow in action.
References to learn about Dataflow
Matthew Roche from Power BI CAT team in Microsoft prepared one of the best set of materials about Dataflow. I highly recommend reading his materials here.
Microsoft MSDN online has also a good set of articles about Dataflow here.
Related posts:
His articles on different aspects of technologies, especially on MS BI, can be found on his blog: https://radacad.com/blog.
He wrote some books on MS SQL BI and also is writing some others, He was also an active member on online technical forums such as MSDN and Experts-Exchange, and was a moderator of MSDN SQL Server forums, and is an MCP, MCSE, and MCITP of BI. He is the leader of the New Zealand Business Intelligence users group. He is also the author of very popular book Power BI from Rookie to Rock Star, which is free with more than 1700 pages of content and the Power BI Pro Architecture published by Apress.
He is an International Speaker in Microsoft Ignite, Microsoft Business Applications Summit, Data Insight Summit, PASS Summit, SQL Saturday and SQL user groups. And He is a Microsoft Certified Trainer.
Reza’s passion is to help you find the best data solution, he is Data enthusiast.
Getting data from a data warehouse that isn’t quite in a usable form (for example, a date table with a non-contiguous value to replace null).
Getting data from websites is a good use case to me. Since the data is static I wonder if this would protect internal data from getting exposed as well.
Good article Reza. I am trying to think of differences/ justifications of using Power BI Data Flows over Azure Data Factory. Maybe they will merge. Do you have any thoughts?
Hi Ian.
Good question. There are pros and cons for each technology. For example, ADF is good with the distribution of computing power over multiple spark clusters, and Dataflow is good with the transformative power of the Power Query. However, for the future plan, I think we still have to wait for few months to see what is going to be the direction.
Very useful Reza, at least now I understand what the data flow is, in simple a way.
Thanks
Hi Reza. I have a use case which I proposed to business some time back. As you know Power BI does not support write-backs yet. There was a requirement wherein the client wanted to do some adjustments to the data by himself. We stripped Power Query from the report and into a data flow. And now, the client(who is good at excel) can himself do the cleansing on his own. It saves us a lot of to and fro emails, time and flexibility. There are workarounds for data correction but none that I found which make make more sense than this.
Hi Sourav
Thanks for sharing your experience. So you’ve isolated the query that is fetching data from the dataset which client changes much, and you run that transformation set separately through Dataflow, and then get data from it in the Power BI. Interesting use case 🙂
Cheers
Reza
Great post. Could you show the versioning you mentioned? I have not thought about this before but it sounds interesting? How exactly would this work? Copying the dataflow and then never update that version?
Yes, I’ve been thinking of running Dataflows manually at specific point-of-time to get snapshots of data. With later support of running Dataflow through REST API, this process can be implemented even much better.
I’ve just started building a small API using MS Flow and Azure Functions to achieve data versioning, saving data into OneDrive – this would be an absolute lifesaver if I could just use Dataflows and M to take care of it.
How do you actually save Dataflows into different locations each time?
Hi Sam
Dataflows can be also configured with Power BI REST API. and in the REST API, you can rename the entity each time to end up with a different destination each time and then run it. However, this is all theory so far, because I haven’t written a code to do it. maybe a good topic for another blog post 🙂
Cheers
Reza
Good article Reza, Data flow may be useful for smaller data sets with limited data source connectivity. Also Not sure how azure data lake optimizes data retrieval (block box for someone without azure data lake license) ? As some one already commented here, azure data factory may be good option for ETL than this.
Hi Chandra.
You can actually SEE how Dataflow manages the data storage in the data lake. If you use your own Azure Data Lake storage, you can connect a Gen2 storage to a dataflow. I will write about it. However, as I mentioned before, for parallel compute processing or big data scenarios, still ADF seems a better option
Cheers
Reza