Miguel’s profile picture is even older than Matthew’s profile picture
All snark aside, Miguel and the whole dataflows team have been awfully busy, and it’s great to see their work available to Power BI authors. I won’t attempt to repeat what’s in the announcement, but I will highlight the new capabilities that have me most excited:
Support for data profiling in Power Query Online – we’ve had this in Power BI Desktop for a while, but it’s just as important for dataflows as it is for datasets.
Better support for files and folders – a lot of the data I play with these days is in folders full of text files, and Power Query Online hasn’t had the best experience for working with this type of data.
Better support for query parameters – there are lots of scenarios where having parameterized queries makes working with dataflows easier, and now Power Query Online makes it easier to work with query parameters.
Do yourself a favor and check out the whole list. Odds are there’s something you’ve been waiting for that will excite you as much as these new capabilities excite me.
And I can’t wait to hear what they are…
 No, I don’t believe that’s possible either, but it is nice to see that you’ve been paying attention.
 Very little of my actual work involves data prep these days, so I need to find data to play with to avoid getting too bored.
Back in July the Power BI team announced the availability of a new connector for Azure Data Lake Storage Gen2.
In recent weeks I’ve been starting to hear questions that sound like these:
Question: Is this ADLSg2 connector how you get to the data behind dataflows?
Answer: No. Dataflows are how you get to the data behind dataflows.
Question: Is this how I can access dataflows if I don’t use Power BI Premium?
Answer: No. Dataflows are not a Premium-only feature.
Question: Can I use the ADLSg2 connector to work with CDM folder data?
Answer: Yes, but why would you?
If your data is already in CDM folders, using the ADLSg2 connector simply adds effort to consuming it in Power BI. You’ll be working with raw, untyped text files instead of working with strongly typed entities.
If your ADLSg2 data is already in CDM folders, strongly consider attaching the CDM folder as a dataflow. This means less up-front work for you, and less ongoing work for the users who need to get insights from the data.
Question: Why do we need an ADLSg2 connector if we have dataflows?
Answer: Now that is a good question!
Power BI dataflows store their data in CDM folder format, and they can be configured to store those CDM folders in your organization’s ADLSg2 data lake. In addition to this, you can attach a CDM folder in ADLSg2 as an external dataflow, making its data available to Power BI users even though the data ingress is taking place through another tool like Azure Data Factory.
But ADLSg2 is much, much more than a repository for dataflows or CDM folders. ADLSg2 supports all sorts of file and blob data, not just CDM folders. And sometimes you need to work with that data in Power BI.
The ADLSg2 connector exists for these scenarios, when your data is not stored in CDM folders. With this connector, users in Power BI Desktop can connect to ADLSg2 resources and work with the files they contain, similar to the existing HDFS and Folder connectors.
 Yes, this is another catch-up post that has been waiting to be finished. No, I do not have any reason to believe that 2020 will be any more forgiving than 2019 has been.
 I could have linked to the product documentation or the official product page, but I believe that Melissa‘s blog does the best job summing up ADLSg2 in a single post.
This week’s Power BIte is the first in a series of videos that present different ways to create new Power BI dataflows, and the results of each approach.
When creating a dataflow by defining new entities in Power Query Online, the final dataflow will have the following characteristics:
Data ingress path
Ingress via the mashup engine hosted in the Power BI service
Data stored in the CDM folder defined for the newly created dataflow
The dataflow is refreshed based on the schedule and policies defined in the workspace
Let’s look at the dataflow’s model.json metadata to see some of the details.
At the top of the file we can see the mashup definition, including the query names and load settings on lines 11 through 19 and the Power Query code for all of the entities on line 22.
At the bottom of the file we can see information about the refresh and storage. Line 26 identifies the entity as a LocalEntity, which means that the entity’s data is physically stored in the current CDM folder.
Line 30 shows that the entity is fully refreshed rather than incrementally refreshed, and line 31 shows the file name where the entity data is stored. Lines 97 through 99 identify the single partition where the data for the current version of the entity is stored, including the full URI for the data file. If this entity used incremental refresh, there would be multiple partitions to match the incremental refresh policy.
If this information doesn’t all make sense just yet, please hold on. We’ll have different values for the same attributes for other dataflow creation methods, and then we can compare and contrast them.
I guarantee it will make as much sense as anything on this blog.
 New videos every Monday morning!
 The same information is also included starting on line 103 for the Promotions entity, but is not pictured here.
This is still one of the most common dataflows questions: what’s the difference between Power BI dataflows and Power BI datasets?
For the last year I have resisted tackling this question head-on. This isn’t because it’s a bad or “dumb” question. Just the opposite – this is a very simple question, and the simpler a question is, the more complex and nuanced the answer is likely to be.
If you’re a regular reader of this blog, you probably already know the answer, because I’ve answered it already. Sort of. The existing answer is distributed across dozens of posts, and if you’ve read all of them and picked up the answer along the way. But I keep hearing this question, and I keep thinking that there must be a more direct answer I could share.
Here it is, in a single, simple table.
Power BI dataflows
Power BI datasets
Analysis Services tabular model
Common Data Model – model.json
Power Query Online
Power Query in Power BI Desktop
Acts as data source in multiple datasets
Shared datasets across workspaces
Scope of reuse
Entity level reuse
Dataset level reuse
Mashup with other data sources
Used for reporting
Reuse outside Power BI
Yes, through ADLSg2
Yes, through XMLA
Data access methods
Certification and promotion
What else am I missing?
Please let me know!
Seriously, you should let me know.
Update: I’ve added a few rows to the table after the post was originally published, to incorporate feedback from readers on differences I had missed. Thank you!
Each of the rows in this table could easily be an in-depth topic in and of itself, so if you’re looking at any of them and thinking “that’s not quite right” I might very well agree with you. There’s a lot of context and a lot of nuance here, and we’re trying to sum things up in a word or two… which is kind of the whole point.
Oh yeah, there’s a video too.
I can’t wait to hear what you think!
 A simple table with ten footnotes.
 The storage aspect of dataflows and datasets is one of the most significant differences between the two. Datasets use the Vertipaq column store to load data into an optimized and highly compressed in-memory representation that is optimized for analysis. Dataflows use text files in folders, which are optimized for interoperability.
 The Analysis Services Tabular engine uses the BI Semantic Model (BISM) to represent its metadata. This is a metadata model originally included in SQL Server 2012 Analysis Services, and used by the Tabular engine ever since.
 Saying “this is the primary purpose” of any complex tool is fraught with risk, because no matter what you say, there are other valid things that remain unsaid. With this said… the big gap that dataflows close is that of self-service data preparation for the purpose of data sharing and reuse. Power BI has always had self-service data preparation through Power Query, but before dataflows the data that was prepared was “locked” in a dataset, for analysis, and not for sharing or reuse.
 Once you have loaded data into dataflows, authorized users can reuse entities from multiple dataflows, and use them as the building blocks for new dataflows or new datasets. Once you have loaded data into a dataset (and published it to the Power BI service) you can enable users to connect to it.
 With dataflows, users can pick and choose the entities they want, but a dataset can only be reused as-is.
 Dataflow entities can be used as data sources in the same Power BI Desktop file as other data sources, and can serve as part of a mashup or composite model, but a dataset can only be reused as-is.
 Although you can obviously use dataflows for reporting, you do so by first importing the data from the dataflow into a dataset.
 It’s interesting to point out that using your own organizational ADLSg2 account does not require Power BI Premium, but using the XMLA endpoint to connect to Power BI datasets from non-Power BI clients does.
 You can only import data into your dataflow entities, but tables in your dataset can import data or use DirectQuery, and a dataset can use a combination of the two.
 You can only import data from a dataflow into a dataset. When connecting to a shared dataset you can only use Live Connections.
 I’ve been thinking of making videos to supplement this blog for almost as long as I’ve been hearing the question that inspired this post. Please take a moment to share your thoughts on the video. This is something of a “soft launch” and although I have plans for a few dozen more videos already, your feedback will be a main factor in how the video series evolves.
In a recent post I mentioned an approach for working around the import-only nature of Power BI dataflows as a data source in Power BI Desktop, and in an older post I shared information about the enhanced compute engine that’s currently available in preview.
Some recent conversations have led me to believe that I should summarize a few points about dataflows and query folding, because these existing posts don’t make them easy to find and understand.
When accessing dataflow entities from Power BI Desktop, no query folding takes place, even if the enhanced compute engine is enabled.
When accessing dataflow entities from other entities in the Power BI service, no query folding takes place unless the enhanced compute engine enabled.
When accessing dataflow entities from other entities in the Power BI service, query folding will take place when the enhanced compute engine is enabled, because the linked entity’s query will be executed against the cached data in SQL, rather than the underlying CDM folder.
These three statements summarize how query folding works – or does not work – in Power BI dataflows today.
The Power BI team has discussed some of their plans for the enhanced compute engine, so this should change in the future  but as of today, the only dataflows scenario where query folding takes place is when a dataflow is backed by the enhanced compute engine is referenced by a linked entity.
I hope this helps clarify things, at least a little…
 I think this will be around the time the engine goes GA, but I don’t remember for sure, and I’m too lazy to re-watch the MBAS session to double check. If you watch it and let me know, I’ll gladly update this post with the details.
I received today what is becoming a common question:
I have a customer who is using dataflows to populate a 255M row fact table, refreshing the last 2 months of data (which is working) but experiencing issues when trying to connect to that Dataflow using Power BI Desktop. Suggestions?
Power BI dataflows are an import-only data source when being used from Power BI Desktop. This means that if you’re building a model in Power BI Desktop and are using dataflows as a source, all of the data from the entities you select will be downloaded from Azure and loaded into your data model.
All of it.
To exacerbate the challenge, dataflows – and the CDM folders on which they’re built – do not support query folding, so even if you filter the records in your query, that filter will be applied in Power BI Desktop, not at the sources.
If one of these entities includes very wide records and/or a very large number of records, this can result in a slow and frustrating experience in Power BI Desktop, and in some circumstances can cause the data load to fail. So what can you do?
My preferred approach is to use a pattern I’ve long used when working with other data sources that don’t support DirectQuery or query folding: work with a subset of the data in Power BI Desktop, and then work with the full data set after you publish to the Power BI service.
For dataflows, it looks like this:
Develop and test the dataflows in the Power BI service
Add filters to the too-large dataflow entities’ queries to reduce the number of records in the entities
Refresh the dataflow
Develop the model and reports in Power BI Desktop
Publish the model and reports to the Power BI service
Remove the filters from the filtered dataflow entities
Refresh the dataflow
Refresh the dataset
The dataflows team announced at MBAS in June that the enhanced compute engine currently in preview will be enable DirectQuery access and query folding when using dataflows from Power BI Desktop at some point – but that point is not today. Until then, I hope this quick pattern will help…
 Like large folders full of large files.
 This is typically just the one or two largest entities, not all of them.
How would you decide how big or how small to make each artifact in the lineage, in terms of the amount of transformations taking place inside the artifact? In my case they would only be shared with 2-3 other users.
For instance I could go all out and have every step that would previously take place in a query editor result in a new link in the data lineage chain, but that would probably be overkill.
I agree that “one step per dataflow” would be overkill, but beyond that the answer is largely “it depends.”
The approach I generally take is to break the end to end data preparation down into blocks that look like this:
Staging – getting the source data into the system (in this case dataflow, but could be data mart, data warehouse, data lake, etc.) with zero or minimal transformations
Cleansing – correcting known data quality and format problems from the staged data
Transformation 1 – getting the cleansed data into the shape required for intended downstream purposes
Enrichment – adding data from other sources, which have ideally already gone through steps 1 through 3
Transformation 2 – getting the cleansed and enriched data into the shape required for analysis, typically as dimensions and facts
The final step may also be performed in the queries that are used to create the final tabular model when creating a dataset in Power BI Desktop. If a given dimension is likely to be used in multiple datasets, implement it as a dataflow entity. If it isn’t, implement it as a table in your dataset.
These guidelines tend to create a moderate number of easily maintainable entities, but they’re obviously the bare minimum – take what works for you, and discard the rest.
I feel like I’m dating myself with this link, but I definitely recommend looking at the Kimball Group’s techniques for data warehousing and BI: resources link. Ralph Kimball and his amazing team know more about this stuff than I will ever forget (or something like that) and there’s a huge volume of guidance available. Do yourself a favor and check it out.
 I assume there are newer resources out there, but when I was your age it was the Kimball Method or the… synonym for highway that rhymes with method.