Power BI dataflows have included capabilities for data lineage since they were introduced in preview way back in 2018. The design of dataflows, where each entity is defined by the Power Query that provides its data, enables a simple and easy view into its data lineage. The query is the authoritative statement on where the entity’s data comes from, and how it is transformed.
In early 2019 dataflows also introduced a graphical lineage view. Users could now easily see and understand the relationships between all dataflows in a workspace, which made it easier than ever to work with linked and computed entities and to take advantage of the Lego-like capabilities of dataflows as building blocks for BI.
But what about everything else in a workspace? What about datasets, and reports, and dashboards? What about them?
Power BI has your back.
Late last month the Power BI team released a new preview capability that lets users view workspace content in a single end-to-end lineage view, in addition to the familiar list view.
Once the lineage view is selected, all workspace contents – data sources, dataflows, datasets, dashboards, and reports – are displayed, along with the relationships between them. Here’s a big-picture view of a workspace I’ve been working in lately:
There’s a lot to unpack here, so I’ll break down what feels to me like the important parts:
- The primary data source is a set of text files in folders. The text files are produced by various web scraping processes, and each has a different format and contents.
- The secondary data source is a set of reference and lookup data stored in Excel workbooks in SharePoint Online. These workbooks contain manually curated data that is used to cleanse, standardize and/or enrich the data from the primary data.
- The primary data is staged with minimal transformation in a “raw” dataflow. This data is then progressively processed by a series of downstream dataflows, including mashing up with the secondary data from Excel, and reshaped into facts and dimensions.
- There is one dataset based on the fact and dimension entities, and report based on this dataset. There’s a second dataset that includes data quality metrics from entities in multiple dataflows, and a report based on this dataset. And there are two dashboards, one that includes only visuals for data quality metrics, and one that presents the main data along with a few tiles from the quality report.
That overview is simplified enough as to be worthless from a technical understanding perspective, but it’s still a wall of text. Who wants to read that?
For a real-world workspace that implements a production BI application, there is likely to be more complexity, and less well defined boundaries between objects. How do you document the contents of a complex workspace, and the relationships between those components? How do you understand them well enough to identify and solve problems?
That’s where the lineage view comes in.
Let’s begin by looking at the data sources.
For data sources that use a gateway, I can easily see the gateway name. For other data sources I can see the data source location. We’re off to a good start, because I have a single place to look to see where my data is coming from.
Next, let’s look at the dataflows.
In addition to being able to see the dataflows and the dependencies between them, you can click on any dataflow to see the entities it contains, and can jump directly to edit the dataflow from this view.
This part of workspace lineage isn’t completely new – this is essentially what you could do with dataflows already. But now you can do it with datasets, reports, and dashboards as well.
Selecting a dataset shows me the tables it contains, and selecting a dashboard or report takes me directly to the visualization. But the real power of this view comes from the relationships between objects. The relationships are where data lineage comes to the fore.
The two primary questions asked in the context of data linage are around upstream “where does this data come from?” and downstream “where is this data used?” lineage scenarios.
The first question is often asked in the context of “why am I not seeing what I expect to see?” and the resulting investigation looks at upstream logic and data source to identify the root cause of problems.
The second question is often asked in the context of “what might break if I change this?” and the resulting investigation looks at downstream objects and processes.
The lineage view has a simple way to answer both questions: just click on the “double arrow” icon and the view will change to highlight all upstream and downstream objects. In a single click you can see where the data comes from, and where the data is used. Click again, and the view toggles back to the default view.
There’s more to lineage view than this, including support for shared and certified datasets, but this should be enough to get you excited. Be sure to check out the preview documentation as you check out the feature!
Update: We now have a video to supplement the blog post. Check it out!
Update: The Power BI blog now has the official announcement for this exciting feature. The blog post includes a look at where the lineage team is planning to invest to make this feature even better, and that all of the information in the lineage view is now available using the Power BI API. If you want to integrate lineage and impact analysis into your own tools, or if you want to build a cross-workspace lineage view, you now have the APIs you need to be successful!
 This is a pet project that may one day turn into a viable demo, assuming work and life let me devote a little more time to it…
 Different, annoying, and difficult to clean up.
 For example, the source web site allows any user to contribute, and although the contribution process is moderated there is no enforcement of content or quality. One artist may be credited for “guitar” on one album, “guitars” on another, “lead guitar” on a third. This sounds pretty simple until you take into account there were close to 50,000 different “artist roles” in the raw source data, that needed to be standardized down to a few hundred values in the final data model.
 I sure hope this gif works!
 This is the most exciting part for me.