Press "Enter" to skip to content

Category: Power BI

Deploying To Power BI Report Server Using Powershell

Published 2018-08-21 by Kevin Feasel

Rob Sewell shows us how to automate Power BI Report Server deployments:

But I dont want to have to do this each time and there will be multiple pbix files, so I wanted to automate the solution. The end result was a VSTS or TFS release process so that I could simply drop the pbix into a git repository, commit my changes, sync them and have the system deploy them automatically.

As with all good ideas, I started with a google and found this post by Bill Anton which gave me a good start ( I could not get the connection string change to work in my test environment but this was not required so I didnt really examine why)

I wrote a function that I can use via TFS or VSTS by embedding it in a PowerShell script.

Click through for the script.

Comments closed

Anti-Joins In Power BI

Published 2018-08-21 by Kevin Feasel

Reza Rad explains when you might want to use anti-joins in Power BI:

Finding rows that are in one table, but not the other is one of the most common scenarios happening in any data related applications. You may have customer records coming from two sources, and want to find data rows that exist in one, but not the other. In Power Query, you can use Merge to combine data tables together. Merge can be also used for finding mismatch records. You will learn through this blog post, how in Power Query you can find out which records are missing with Merge, and then report it in Power BI. To learn more about Power BI, read Power BI book from Rookie to Rock Star.

Read on for a demo of how to use anti-joins to solve this problem.

Comments closed

Performing Linear Regression With Power BI

Published 2018-08-16 by Kevin Feasel

Jason Cantrell shows how to create a simple linear regression in Power BI:

Linear Regression is a very useful statistical tool that helps us understand the relationship between variables and the effects they have on each other. It can be used across many industries in a variety of ways – from spurring value to gaining customer insight – to benefit business.

The Simple Linear Regression model allows us to summarize and examine relationships between two variables. It uses a single independent variable and a single dependent variable and finds a linear function that predicts the dependent variable values as a function of the independent variables.

If you want real linear regression, drop in an R or Python script.

Comments closed

Considerations When Using Sort By Column In DAX

Published 2018-08-15 by Kevin Feasel

Marco Russo shows us some things to keep in mind when using Sort By Column in DAX:

A query in MDX automatically inherits the correct column sort order from the data model; the result of an MDX query is always sorted according to the Sort By Column setting. However, DAX does not have any implicit sort order for the columns other than the natural sort order of the underlying data type. For this reason, a DAX query must always specify the sort order in an ORDER BY condition – similarly to a query in SQL. Because DAX requires for a column used in ORDER BY to be part of the query result, a Power BI visual that sorts a column always generates a query that includes at least two columns: the column requested in the report and the underlying column used in the Sort By Column setting. In other words, a Power BI visual showing data by Month must generate a query that contains both Month Name and Month Number, whereas MDX only requires the Month Name column.

There’s also an interesting example where Power BI behaves differently from Excel.

Comments closed

Aggregations In Power BI

Published 2018-08-15 by Kevin Feasel

Teo Lachev takes us on a tour of aggregates in Power BI:

During the “Building a data model to support 1 trillion rows of data and more with Microsoft Power BI Premium” presentation at the Business Applications Summit, Microsoft discussed the technical details of how the forthcoming “Aggregations” feature can help you implement fast summarized queries on top of huge datasets. Following incremental refresh and composite models, aggregations are the next “pro” feature that debuts in Power BI and it aims to make it a more attractive option for deploying organizational semantic models. In this blog, I summarize my initial observations of this feature which should be available for preview in the September release of Power BI.

Aggregations are not a new concept to BI practitioners tackling large datasets. Ask a DBA what’s the next course of action after all tricks are exhausted to speed up massive queries and his answer would be summarized tables. That’s what aggregations are: predefined summaries of data, aimed to speed queries at the expense of more storage. BI pros would recall that Analysis Services Multidimensional (MD) has supported aggregations for a long time. Once you define an aggregation, MD maintains it automatically. When you process the partition, MD rebuilds the partition aggregations. An MD aggregation is tied to the source partition and it summarizes all measures in the partition. You might also recall that designing proper aggregations in MD isn’t easy and that the MD intra-dependencies could cause some grief, such as processing a dimension could invalidate the aggregations in the related partitions, requiring you to reprocess their indexes to restore aggregations. On the other hand, as it stands today, Analysis Services Tabular (Azure AS and SSAS Tabular) doesn’t support aggregations. Power BI takes the middle road. Like MD, Power BI would search for suitable aggregations to answer summarized queries, but it requires more work on your part to set them up.

Aggregates in Power BI aren’t as simple as they were in Analysis Services Multidimensional, but they do exist, and hopefully the Power BI team will improve upon them in future versions.

Comments closed

5 Reasons To Like Power BI Composite Models

Published 2018-08-14 by Kevin Feasel

Devin Knight has a video explaining why it’s worth getting excited over Power BI Composite Models:

Another special new capability that we gain with Composite Models is native support for many-to-many relationships. I see this as incredibly helpful to new Power BI users that are not familiar with strict requirements around relationships.

Traditionally, Power BI is only able to create relationships that are one-many. This means that one of the two tables in a relationship must have a column with unique values for the relationship to work. With this new feature the requirement that a table must have a column with unique values is no longer needed.

For those of you that have a data modeling background this may be a little different than what you traditionally envision a many-to-many relationship should be. Normally a many-to-many relationship includes a bridge table to bring the two tables together, but now with Power BI Composite Models the relationship can be directly defined between them.

Check it out.

Comments closed

A Recommendation Against Using TOTALYTD

Published 2018-08-14 by Kevin Feasel

Marco Russo recommends against using the TOTALYTD function in DAX:

You have been warned. I don’t like the code above. If you want to apply filters, use CALCULATE. Maybe the code written using CALCULATE is verbose and boring, but it’s easier to read. If you are looking for an enjoyable reading for your free time, you can choose a novel rather than playing with an obscure DAX syntax.

So, why writing a blog post about this? The reason is that TOTALYTD has the ability to accept a scalar value as an argument instead of (or after) the filter. This is the same additional optional parameter accepted by DATESYTD in case the fiscal year does not end on December 31st. The problem is that the third argument of TOTALYTD is a filter if you use a predicate or a table expression, and it’s a marker for end of year in case it’s a scalar value. Confusing enough? Ok, let me present some example.

Read the whole thing.

Comments closed

Improving Power Query Performance By Reducing Variables

Published 2018-08-14 by Kevin Feasel

Reza Rad shows us an extreme case of variable fatigue in Power Query:

This is a sample Power Query file, which in that I do a very simple transformation. The transformation is adding one to the existing number. However, in this sample, we are doing it over thousands of steps! one step at a time, we are adding thousands to a number. The main reason to do it this way is to show you what is the performance you get when you have too many variables (or let’s say steps) in Power Query.

Reza reduces the number of steps and turns a 15-minute operation into one which finishes in under a second.

Comments closed

Power BI August Release And SSAS Performance Improvements

Published 2018-08-10 by Kevin Feasel

Chris Webb points out something new in the Power BI August 2018 release:

While I was playing around with the new release (August 2018) of Power BI Desktop I noticed there was an undocumented change: similar to the OData improvements I blogged about here, there is a new option in the AnalysisServices.Database() and AnalysisServices.Databases() M functions that turns on a newer version of the MDX generation layer used by the Power Query engine. Like the OData improvements it is an option called Implementation=”2.0”, used like this:

AnalysisServices.Databases(
	"localhost", 
	[
		TypedMeasureColumns=true, 
		Implementation="2.0"
	]
)

…and also, as with the OData improvements, you will need to manually edit any existing M queries to take advantage of this.

Read on for Chris’s test and analysis of the resulting MDX output.

Comments closed

Dataflows In Power BI

Published 2018-08-08 by Kevin Feasel

James Serra gives us a preview of Power BI Dataflows:

In short, Dataflows integrates data lake and ETL technology directly into Power BI, so anyone with Power Query skills (yes – Power Query is now part of Power BI service and not just Power BI Desktop and is called Power Query online) can create, customize and manage data within their Power BI experience (think of it as self-service data prep).  Dataflows include a standard schema, called the Common Data Model (CDM), that contains the most common business entities across the major functions such as marketing, sales, service, finance, along with connectors that ingest data from the most common sources into these schemas.  This greatly simplifies modeling and integration challenges (it prevents multiple metadata/definition on the same data).  You can also extend the CDM by creating custom entities.  Lastly – Microsoft and their partners will be shipping out-of-the-box applications that run on Power BI that populate data in the Common Data Model and deliver insights through Power BI.

A dataflow is not just the data itself, but also logic on how the data is manipulated.  Dataflows belong to the Data Warehouse/Mart/Lake family.  Its main job is to aggregate, cleanse, transform, integrate and harmonize data from a large and growing set of supported on-premises and cloud-based data sources including Dynamics 365, Salesforce, Azure SQL Database, Excel, SharePoint.  Dataflows hold a collection of data-lake stored entities (i.e. tables) which are stored in internal Power BI Common Data Model compliant folders in Azure Data Lake Storage Gen2.

Also check out the comments for some clarification on why you’d want to use Dataflows rather than doing the work directly in the data lake.

Comments closed