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Category: Power BI

Creating A Date Dimension In Power BI

Reza Rad has released part 1 of a series on creating date dimensions in Power BI:

When it comes to create the date (or calendar) dimension in Power BI, there is always a question: Should I create the dimension with Power Query or DAX? This is a very good question to ask. It means that you know that there are multiple ways of creating it. What is the difference? the answer is that for many scenarios these are similar. So, it might not be different to use Power Query or DAX for it. However, there is a big difference.

Power Query can fetch data from live web APIs. This functionality gives you the power to fetch public holidays live from an API. You cannot do this with DAX! Apart from this big difference, majority of other requirements can be done with both, you can write calculations in both M or DAX to get calendar columns as well as fiscal columns. In many scenarios public holidays plays an important role in analyzing data. You would like to know how the sales was in holidays compared to other holidays and etc.

There are a few more parts forthcoming, so stay tuned.

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Power BI Report Server August Preview

Aaron Nelson gives us a happy report:

Power BI Report Server August 2017 Preview is now available. Think of this a “v.Next” of Power BI Report Server [On-Premises], for all Data Sources.

Here’s an excerpt from the Power BI blog post from this weekend:

With this August 2017 preview, users can create Power BI reports in Power BI Desktop that connect to any data source, and publish their reports to Power BI Report Server. There’s no special configuration required to enable this functionality

Read on for more information and a link to download the latest preview.  It had me as soon as I read “all data sources.”

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Comparing Baselines Using Power BI

Melissa Connors shows how to take baseline data from SentryOne and visualize it within Power BI:

Using Power BI to connect to multiple baselines in SentryOne allows me to make fast comparisons. I previously translated baseline values into charts manually or through some Excel/SQL Server connections. See this post on data compression as an example. I wanted to compare performance between different compression levels (None, Row, and Page), and include the Average, Minimum, Maximum, and Standard Deviation values. Now, I have a standard template that looks better in Power BI. Once I create a baseline in my database, I have access to it in my charts. If you don’t have Power BI, you can use the query from this post in Excel or another reporting method for your comparisons.

Even if you don’t use SentryOne, the principles are generally applicable.

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Power Query: Joining On Date Ranges

Reza Rad shows how to build merge joins between date ranges in Power Query:

Customer’s table has the history details of changes through the time. For example, the customer ID 2, has a track of change. John was living in Sydney for a period of time, then moved to Melbourne after that.

The problem we are trying to solve is to join these two tables based on their customer ID, and find out the City related to that for that specific period of time. We have to check the Date field from Sales Table to fit into FromDate and ToDate of the Customer table.

This is a common type 2 SCD scenario.  I’d be concerned that this solution would not work with large data sets which may already be pushing the size limits of the Vertipaq engine.

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What Is DAX?

Matt Allington covers some of the basics of DAX:

Do I need to learn the DAX language?

You certainly do not need to know how to write DAX to get started with Power BI.  Power BI is the newest business intelligence tool that leverages the DAX language (via Power Pivot) and it is definitely possible to get started and build some reports without learning any DAX at all.   If you are a “consumer of reports” that other people produce for you then you certainly don’t need to learn any DAX.  However if you are someone that wants to do your own adhoc (or structured) analysis of data using Power BI, Power Pivot for Excel, then you will definitely want to learn to write some DAX in order to get value from what these new tools have to offer.

It’s a good intro if you aren’t familiar with DAX.

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Row-Level Security In Power BI

Paul Turley has a video showing how to use row-level security with Power BI:

The best method to implement row-level security in a published Power BI model or SSAS Tabular model consumed from the Power BI service will depend on will depend on your data and requirements.  The method I demonstrate here is one of the most flexible approaches and one that I commonly use in my projects.

Click through to watch the video.

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Power BI With What-If Analysis

Dusty Ryan shows off What-If Analysis in Power BI:

Next, I’m going to create a calculated measure that multiples Revenue Last Year by Revenue Target % Value. Here’s the formula for the calculated measure:

Revenue Target = [Revenue Last Year] * [Revenue Target % Value]

Now I’m going to add this new measure, Revenue Target, to my line chart. And now when I use my slicer slider bar, I can dynamically change my Revenue Target line on the chart!

This is pretty cool.  Definitely check out Dusty’s example; it’s something that might make many an executive happy.

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Putting Measures On Rows In Power BI

Meagan Longoria shifts our perspective by 90 degrees:

Back in January 2016, I wrote a blog post explaining a DAX workaround that allows you to put measures on rows in a matrix in a Power BI report. I’m happy to say that you no longer need my workaround because you can now natively put measures on rows in a matrix in both Power BI Desktop and PowerBI.com.

This is accomplished via a new formatting option for the matrix.

Click through to see how to pull this off.

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Analyzing Twitter Data With Storm In HDInsight

Nischal S shows how to configure an HDInsight cluster to process tweets, followed by loading them into a Power BI dashboard:

When we need to process streams of real-time data, Storm is a great contender. Examples of streaming data are the number of consumer clicks and navigations on a website, IIS or user logs, IoT data, and social network information. In all these scenarios, we use real-time data processing. Apache Storm can process real-time unbounded streams of data.

The term “unbounded” defines streams of data with no start or end. Here, the processing of data is continuous and in real-time. Twitter is a good example. Twitter data is continuous, has no start or end time, and is provided in real-time by millions of Twitter users around the world.

Storm wouldn’t rank in my top three technologies for doing this, but it certainly does the job.

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