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Category: Power BI

Configuring Power BI Incremental Refresh

Published 2020-07-01 by Kevin Feasel

Gilbert Quevauvilliers has a follow-up from a post:

Following on from my successful blog post How you can incrementally refresh any Power BI data source (This example is a CSV File), I found a way where I can just use dates created in Power Query to get data refreshing incrementally.

Full credit goes to Rafael Mendonç who actually figured this out. All that I have done is to translate what Rafael Mendonça did in his PBIX and put it into steps that you can follow along with.

https://www.rafaelmendonca.com/2020/06/incremental-powerbi-csv-api-excel-odbc.html

In this blog post I am going to demonstrate how to get this working with what I hope is very easy to follow.

Read on for the process.

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Tips for Improving Power BI Dashboards

Published 2020-07-01 by Kevin Feasel

Tino Zishiri has a set of tips to design better-looking dashboards:

There are several reasons why you should design great looking dashboards. Here are a few;

– They make information more accessible – end users benefit from an intuitive design that makes insight easy to obtain so they can make informed decisions.
– They help convey your message – you’re in a better position to tell a coherent story. Applying design principles can also help accentuate your message. My colleague Kalina Ivanova has written an excellent series of blogs on Data Storytelling with Power BI.
– They encourage user adoption – if a report is useful to users and has a great look and feel then you’re winning.

In this blog, I’ll briefly cover the building blocks that make up a good Power BI dashboard. I then explore the stepping stones that will level up your dashboard and take it from good to great.

One area where I do have some disagreement is that the Z and F layouts are fine for text-heavy formats, but generally “text-heavy” and “dashboard” don’t go together very well. My preference is the notion of focal points (go about 3/4 of the way down, to the section entitled “Where We Look”), which works much better at describing eye behavior for image-heavy layouts. That aside, I like this post a lot.

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Good Practices for Naming Things in Power BI

Published 2020-06-30 by Kevin Feasel

Chris Webb shares some thoughts on the power of names:

What’s wrong with this picture? Look at the names:

– The tables and columns have the same names that they had in the data source, in this case a SQL Server database. Note the table name prefixes of “Dim” for dimensions and “Fact” for fact tables.
– The column and measure names either don’t have spaces or use underscores instead of spaces.
– What on earth does the measure name _PxSysF even mean?

Chris mentions that some of the ideas in the post may be controversial, but to be honest, I don’t think any of them are. The important thing here is to keep your audience in mind.

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Role-Playing Dimensions in Power BI

Published 2020-06-29 by Kevin Feasel

Martin Schoombee explains the concept of role-playing dimensions and then explains how that works in the Power Bi world:

In technical terms a role-playing dimension is when a dimension table has multiple (foreign key) relationships to the same fact table. In more non-technical terms, it is when you have the same attribute (“Date” for instance) that can relate to the same metrics in different ways.

If you look at the data model below as an example, you can see the Date entity could be used to reference either the Invoice Date or Delivery Date from the Sales entity, changing the perspective of the metrics we’re looking at.

It’s a little surprising to me that there isn’t an easier way to handle this concept. Role-playing dimensions are a core part of the Kimball model, and they’re common enough that you’d expect support to be a bit simpler.

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Pattern-Matching and Text Extraction in Power Query

Published 2020-06-29 by Kevin Feasel

Imke Feldmann shows how we can match specific patterns in Power Query, which lacks regular expresssions:

I plan to approach this by

1. stepping through the string and check each character if it is valid.
2. If so, store it
3. and if not, forget it. And in that case, also forget the stored values so far so the collection starts from scratch.
4. Then if a series of matches builds up, I will have to check the count of the stored values to not exceed the length of my target pattern.
5. Once the length is reached, no further checks shall be performed and the found values be returned.

My aim is to find a generic way so that this solution can be adapted to many other similar use cases as well. Therefore the pattern must be described in a scalable way and the identification of the pattern elements should be easy to compute.

It’s good to be able to adapt, improvise, and overcome, though this is exactly what regular expressions are intended to do. It’s odd that there appears to be no built-in capability, where instead you’d have to do something like bring in external languages like JavaScript.

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More Fun with 1-Column Fusion in DAX

Published 2020-06-26 by Kevin Feasel

Phil Seamark continues a discussion on single-column fusion:

You may notice the [Package – Bag] measure uses the * (multiplication) operator in line 4 between a measure and what looks like a column filter.

The [base measure] performs a simple COUNTROWS aggregation and returns an INTEGER. This side of the equation makes sense, so let’s see what is going on with the DAX on the right-hand side of the * operator? The filter statement is encompassed with parenthesis, which automatically converts the expression to a TRUE/FALSE boolean value – which is then implicitly converted to an INTEGER value of either 0 or 1. It can’t be anything else.

The result of this double conversion is that we end up with a INTEGER * INTEGER to produce the number we see in the visual.

Click through for plenty more where that came from.

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Creating a Time Dimension with Time Bands in Power BI

Published 2020-06-26 by Kevin Feasel

Soheil Bakhshi shares how you can create a time dimension with a granularity of seconds in Power BI and SSAS Tabular:

I wrote some other posts on this topic in the past, you can find them here and here. In the first post I explain how to create “Time” dimension with time bands at minutes granularity. Then one of my customers required the “Time” dimension at seconds granularity which encouraged me to write the second blogpost. In the second blogpost though I didn’t do time bands, so here I am, writing the third post which is a variation of the second post supporting time bands of 5 min, 15 min, 30 min, 45 min and 60 min while the grain of the “Time” dimension is down to second. in this quick post I jump directly to the point and show you how to generate the “Time” dimension in three different ways, using T-SQL in SQL Server, using Power Query (M) and DAX. Here it is then:

Click through for the code, which includes several sample bands (e.g., 5 minutes, 15 minutes) that you can also control.

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Power BI On-Premises Data Gateway Error SpooledOperationMissing

Published 2020-06-23 by Kevin Feasel

Gilbert Quevauvilliers encounters an error:

I got the following error DM_GWPipeline_Gateway_SpooledOperationMissing”,”parameters”:{},”details”:[],”exceptionCulprit”:1

This error was caused because the current Virtual machine where the On-Premise Data Gateway was running was cloned and then started up.

Read on to learn about the implications and how Gilbert was able to solve this issue.

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Differences between Tableau and Power BI Data Models

Published 2020-06-18 by Kevin Feasel

David Eldersveld lays out a few differences between Tableau and Power BI’s data models:

Prior to 2020.2, Tableau had a physical data model that allowed joins between tables. This layer is still present, and it is similar to Power BI’s merge capability within Power Query to join multiple tables into a single table.

With the introduction of the logical model in Tableau 2020.2, a single logical table may consist of one or more physical tables. Two or more logical tables may be related to each other. This approach shares many similarities with Microsoft’s tabular model, but there are currently some core differences that may impact how you approach data modeling in one tool versus another. It’s not always going to be a straightforward scenario to apply knowledge of how to build a data model in one product to building it in the other.

NOTE: This post considers some core differences between Tableau and Power BI modeling as of June 2020. It does not go into detail on announced roadmap features such as Power BI’s enhanced composite models coming later in 2020, which will allow scenarios like combining a “live connection” composite model with additional data sources, or even multiple live connection models.

Read on for four differences David has found.

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Dynamic Measures in DAX

Published 2020-06-17 by Kevin Feasel

Kristyna Hughes explains the concept of dynamic measures in DAX:

For example, you may have three major stakeholders looking at a report: CEO, CFO, and COO. The CEO may be more interested in future profits and looks at trends in the number of loads booked to see where we are headed. The CFO wants to see profit trends on loads that have already been completed (actualized profit). Additionally, the COO would rather see what’s currently in transit and look at only picked up loads. You could make three pages, one based on booked loads for the CEO, another based on delivered loads for the CFO, and one based on picked up loads for the COO. But what happens if your report is already going to be multiple pages and needs room to grow? You would need to triple your page count to accommodate the needs of your users or build three completely separate reports! Don’t worry, dynamic measures can solve this problem for you without clogging up your reports or workspaces with extra measures, visuals, and reports.

Click through for the demonstration on how to combine this with a slicer to change reported measures.

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