Month: July 2019

Choosing the right tool for the job

On By Matthew RocheIn Data Culture, Dataflows, Power BI, Power Query4 Comments

Important: This post was written and published in 2019, and the content below may no longer represent the current capabilities of Power BI. Please consider this post to be an historical record and not a technical resource. All content on this site is the personal output of the author and not an official resource from Microsoft.

A few weeks back[1] I got this comment on my Power BI dataflows overview post:

choosing tool comment

This morning I started to reply to the comment, and by the time I was done I realized that it should be a blog post on its own. So here we go… but before I continue I feel compelled to share this blurb from my blog bio:

I’m a program manager on the Power BI team at Microsoft. This is my personal blog, and all posts and opinions are mine and mine alone, and if you believe my teenage children they are not shared by anyone else in the entire world, because I’m weird and embarrassing.

So… There are a few capabilities that make dataflows “pop” for me. This shouldn’t be taken as a comprehensive list, and isn’t intended to say that dataflows are better than any other data prep tool, but hopefully it will be useful:

  • Power BI dataflows build on the Power Query experience many users are already familiar with. If you know how to use Power Query in one tool, you know how to use it in another.
  • Power BI dataflows are integrated into the end-to-end Power BI service and experience. You’re not pushing data into a CSV file. You don’t need to provision and manage a database. You get strongly typed “tables” of data that can be reused by you or by other users, and it’s all in one seamless experience.
  • Power BI dataflows and CDM folders provide capabilities for bridging the low-code/no-code world of self-service BI with managed central corporate BI in Azure.
  • Power BI dataflows enable Excel-like composition of ETL processes with linked and computed entities.
  • Power BI dataflows can scale beyond the desktop and leverage the power of the cloud to become part of an end-to-end BI application.

But… This is just a list of features.

The right tool for a job depends largely on the context of that job. If you’re trying to say that one tool is better than another, you need to have project/selection/evaluation criteria that everyone[2] agrees on. Only then you can compare multiple tools against those criteria. If you can’t do that, you’re probably just having a popularity contest.

This is one of the reasons why analysts like Gartner and and Forrester play the role that they do – they define and document their criteria, and then do exhaustive research to evaluate tools against those criteria. They take great pains to make sure that their criteria align well with the needs of the industry. They evolve the criteria as the market evolves, and they update their analyses as products evolve.

If you take this type of approach, you’ll probably end up choosing the tool that’s right for the job at hand – even if it’s not the tool you had in mind when you started. It’s not always easy to convince everyone to step back and look at the big picture before making what may feel like a small decision, but when choosing tools and platforms it’s often a good way to save time and effort in the long run.

[1] July has been kicking my butt, even more so than usual this year. Not only have I not been blogging consistently, I’ve basically put everything on hold that wasn’t vitally important and critically urgent. Ugh.

[2] For a given value of “everyone.” Identifying the necessary and appropriate stakeholders is a big enough problem on its own…

Quick Tip: Implementing “IsNumeric” in Power Query

On By Matthew RocheIn Power BI, Power Query7 Comments

I’ve had many people tell me that they have a blog so they will have a place to look to find the information that they need, and answers to their own future questions. This isn’t typically the case for me… but this post is an exception.

Every few months I find myself searching for an IsNumeric function[1] in Power Query. Each time I end up spending a few frustrating hours before I find the solution I’m looking for, because the search terms I’m using aren’t how people who have already solved the problem have shared it.

Last night, when I was once again failing to find the solution I needed I reached out to Twitter for help, and Imke Feldman delivered:.

If you’re not sure how you’d use this solution, consider the problem I was working on last night. My source data includes a “release-date” column that can include a wide range of values, and because I do not have any influence over the source system, I need to work with what it contains. Specifically, I need to find records that contain a year, and I need to extract that year into a new numeric column.

01 data

There are a few more pattern variations in the data including two-digit years, but this sample shows the basic problem I need to solve. And in order to solve it, I need to be able to determine if the two characters at the end of the text represent a number.

Using Imke’s approach, this is what I ended up doing:

  1. Extract the last two characters from the source column
  2. When the extracted characters contain a number, convert them to numeric
  3. Standardize the resulting value to represent the correct year

This last step is specific to my particular “date as year encoded in different text formats” problem, but I’m including it here in case someone else may find it useful.

It looks like this:

02 solution

Thanks to Imke for her quick response. Hopefully there are people out there other than me who will find this useful… but I know that I will.

[1] This is how I think of a function that accepts a text value returns true if that value contains a number, and false if it does not. This is probably because of my decades-long love affair with Transact-SQL, since T-SQL has an ISNUMERIC function built in.

Are you building a BI house of cards?

On By Matthew RocheIn Data Culture, Data Governance, Dataflows, Patterns, Power BI, Power Query10 Comments

Every few weeks I see someone asking about using Analysis Services as a data source for Power BI dataflows. Every time I hear this, I cringe, and then include advice something like this[1] in my response.

Using Analysis Services as a data source is an anti-pattern – a worst practice. It is not recommended, and any solution built using this pattern is likely to produce dissatisfied customers. Please strongly consider using other data sources, likely the data sources on which the AS model is built.

 

There are multiple reasons for this advice.

 

Some reasons are technical. Extraction of large volumes of data is not what an Analysis Services model is designed for. Performance for the ETL process is likely to be poor, and you’re likely end up with memory/caching issues on the Analysis Services server. Beyond this, AS models typically don’t include the IDs/surrogate keys that you need for data warehousing, so joining the AS data to other data sources will be problematic.[2]

 

For some specific examples and technical deep dives into how and why this is a bad idea, check out this excellent blog post from Shabnam Watson. The focus of the post is on SSAS memory settings, but it’s very applicable to the current discussion.

 

Some reasons for this advice are less technical, but no less important. Using analytics models as data sources for ETL processing are a strong code smell[3] (“any characteristic in the source code of a program that possibly indicates a deeper problem”) for business intelligence solutions.

 

Let’s look at a simple and familiar diagram:

 

01 good

 

There’s a reason this left-to-right flow is the standard representation of BI applications: it’s what works. Each component has specific roles and responsibilities that complement each other, and which are aligned with the technology used to implement the component. This diagram includes a set of logical “tiers” or “layers” that are common in analytics systems, and which mutually support each other to achieve the systems’ goals.
Although there are many successful variations on this theme, they all tend to have this general flow and these general layers. Consider this one, for example:

 

02 ok

This example has more complexity, but also has the same end-to-end flow as the simple one. This is pretty typical for  scenarios where a single data warehouse and analytics model won’t fulfill all requirements, so the individual data warehouses, data marts, and analytics models each contain a portion – often an overlapping portion – of the analytics data.

Let’s look at one more:

03 - trending badly

This design is starting to smell. The increased complexity and blurring of responsibilities will produce difficulties in data freshness and maintenance. The additional dependencies, and the redundant and overlapping nature of the dependencies means that any future changes will require additional investigation and care to ensure that there are no unintended side effects to the existing functionality.

As an aside, my decades of working in data and analytics suggest that this care will rarely actually be taken. Instead, this architecture will be fragile and prone to problems, and the teams that built it will not be the teams who solve those problems.

And then we have this one[4]:

04 - hard no

This is what you get when you use Analysis Services as the data source for ETL processing, whether that ETL and downstream storage is implemented in Power BI dataflows or different technologies. And this is probably the best case you’re likely to get when you go down this path. Even with just two data warehouses and two analytics models in the diagram, the complex and unnatural dependencies are obvious, and are painful to consider.

What would be better here?[5] As mentioned at the top of the post, the logical alternative is to avoid using the analytics model and to instead use the same sources that the analytics model already uses. This may require some refactoring to ensure that the duplication of logic is minimized. It may require some political or cross-team effort to get buy-in from the owners of the upstream systems. It may not be simple, or easy. But it is almost always the right thing to do.

Don’t take shortcuts to save days or weeks today that will cause you or your successors months or years to undo and repair. Don’t build a house of cards, because with each new card you add, the house is more and more likely to fall.

Update: The post above focused mainly on technical aspects of the anti-pattern, and suggests alternative recommended patterns to follow instead. It does not focus on the reasons why so many projects are pushed into the anti-pattern in the first place. Those reasons are almost always based on human – not technical – factors.

You should read this post next: http://workingwithdevs.com/its-always-a-people-problem/. It presents a delightful and succinct approach to deal with the root causes, and will put the post you just read in a different context.

[1] Something a lot like this. I copied this from a response I sent a few days ago.

[2] Many thanks to Chris Webb for some of the information I’ve paraphrased here. If you want to hear more from Chris on this subject, check out this session recording from PASS Summit 2017. The whole session is excellent; the information most relevant to this subject begins around the 26 minute mark in the recording. Chris also gets credit for pointing me to Shabnam Watson’s blog.

[3] I learned about code smells last year when I attended a session by Felienne Hermans at Craft Conference in Budapest. You can watch the session here. And you really should, because it’s really good.

[4] My eyes are itching just looking at it. It took an effort of will to create this diagram, much less share it.

[5] Yes, just about anything would be better.

Dataflows in Power BI: Overview Part 9 – Lineage and impact analysis

On By Matthew RocheIn Data Governance, Dataflows, Metadata, Power BI11 Comments

Important: This post was written and published in 2019, and the content below may no longer represent the current capabilities of Power BI. Please consider this post to be an historical record and not a technical resource. All content on this site is the personal output of the author and not an official resource from Microsoft.

When you build a Power BI dataflow, lineage is built in. It’s not an added feature – it’s a fundamental aspect of how dataflows work. Instead of having an ETL process that performs the data movement and compute in one place and the data storage in another[1], dataflow entities are defined by a Power Query “M” query. The ETL logic in the query and the data storage are defined as a single unit[2]. The result is that not only does a dataflow contain the data, it contains the full lineage about where the data came from and how it was transformed.

Update: The user experience shown in this post is from July 2019, and is no longer current. For a more current view of lineage in Power BI workspaces, please see this post and video from October 2019, and the Power BI documentation.

lineage today

This lineage information is what makes possible the diagram view that’s available today. As shown above, the diagram view provides a workspace-level lineage view of all dataflows in the workspace, their relationships, and the data sources from which they extract data. – including data sources that are dataflows in other workspaces.

It’s worth emphasizing that this diagram view isn’t an editor per se – it’s not where you define lineage relationships, it’s where you can view, explore, and understand the lineage relationships that are automatically created when you build your dataflows. At the same time, the diagram view does let you edit and manage the dataflows in your workspace in the same way as the list view.

The dataflows API lets developers build similar experiences and automated processes. Specifically, the Get Dataflow API returns the model.json file that contains all the dataflow metadata, including the M scripts for the queries that define the dataflow entities.

Of course, dataflows are just one part of a complete lineage and impact analysis story in Power BI. Ideally a Power BI workspace administrator would be able to see all data sources, dataflows, datasets, reports, and dashboards in a single view, and to easily navigate and manage the relationships and dependencies between them. Something like this:

lineage tomorrow

At the Microsoft Business Applications Summit in June, Microsoft shared plans for lineage across all artifacts in a workspace. This represents a continuation of the current functionality available in dataflows and of the recently-announced shared and certified datasets.

With this upcoming functionality, users working with reports and dashboard can easily see where the data comes from, and how and where it has been transformed. I’m hesitant to call this the “holy grail” of business intelligence, but I’ve heard it described this was enough times that I wouldn’t argue too loudly if someone did. If I a dollar for every meeting I’ve been in where the agenda was derailed by and argument about whether the data was right[3]… In any event, this is a very common problem, with few good solutions.

How are you using the lineage capabilities in dataflows today? Do you have processes that rely on the dataflows lineage UI or API? I’d love to hear about what you’re doing now and what you’re planning – let me know.

[1] Such as having a batch job, stored procedure, or SSIS Data Flow that loads into a SQL Server table or HDFS folder.

[2] If you’re not familiar with this aspect of Power BI dataflows, I’d recommend reading through these posts to catch up: Dataflows in Power BI: Overview Part 6 – Linked and Computed Entities, Lego Bricks and the Spectrum of Data Enrichment and Reuse, Dataflows in Power BI: Resources, and maybe Dataflows in Power BI.

[3] Then I could afford the upgraded hosting plans that WordPress keeps pushing at me…