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«November 2015»

DirectQuery in Power BI Desktop

In the latest Power BI Desktop a new Preview features was released that now allows you to connect using DirectQuery to either SQL Server or Azure SQL Databases.  DirectQuery is a really neat feature that allows you to point to the live version of the data source rather than importing the data into a data model in Power BI Desktop. 

Normally when you want to get an updated dataset in the Power BI Desktop you would have to manually click the refresh button (this can be automated in the Power BI Service), which would initiate a full reimport of your data.  This refresh could take a variable amount of time depending on how much data your have.  For instance, if you’re refreshing a very large table you may be waiting quite a while to see the newly added data. 

With DirectQuery data imports are not required because you’re always looking at a live version of the data.  Let me show you how it works!

Turning on the DirectQuery Preview

Now, because DirectQuery is still in Preview you must first activate the feature by navigating to File->Options and settings->Options->Preview Features then check DirectQuery for SQL Server and Azure SQL Database


Once you click OK you may be prompted to restart the Power BI Desktop to utilize the feature.

Using DirectQuery in Power BI Desktop

Next make a connection either to an On-Premises SQL Server or Azure SQL database.

Go to the Home ribbon and select Get Data then SQL Server.


Provide your Server and Database names then click OK. ***Do not use a SQL statement.  It is not currently supported with DirectQuery***


From the Navigator pane choose the table(s) you would like to use.  I’m just going to pick the DimProduct table for this example and then click Load.  You could select Edit and that would launch the Query Editor where you could manipulate the extract.  This would allow you to add any business rules needed to the data before visualizing it.


Next you will be prompted to select what you want to connect to the data. Again, Import means the data

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The Big Data Blog Series

Over the last few years I’ve been speaking a lot on the subject of Big Data. I started by giving an intermediate session called “Show Me Whatcha’ Workin’ With”. This session was designed for people who had attended a one hour introductory session that showed you how to load data, to look at possible applications … Continue reading The Big Data Blog Series
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Why Do I Like (LOVE) My Job #MEME15

  • 16 August 2012
  • Author: Kathi Kellenberger
  • Number of views: 2641

It looks like I am a day late to completely follow the rules, but I thought I would chime in anyway on #Meme15. Why do I love my job? If I have talked to you in person lately, you probably know, because you couldn’t get me to stop talking about it!

Here are the top 5 reasons I love my job as a consultant with Pragmatic Works:

5) I love NOT COMMUTING. For 15 years, I commuted from Edwardsville, IL, to St. Louis, and now I get to work from my home.  I am not sure what I am doing with my extra 2+ hours each day, maybe it is just getting more sleep.

4) I travel two weeks per month on average to client sites. So far, the travel has been fun because I know people all over the country to meet up with during the evening. For example, I had dinner with Tim Mitchell and his wife while working near Dallas and met with Rick Morelan while working in Bothel, WA. I get to go to cool restaurants and see the sites. Except for working for the client, it’s almost like a vacation.  I knew that I would have so much fun in Phoenix that I didn’t even come home during the weekend and stayed there a straight two weeks.   I was not disappointed!

3) Pragmatic Works not only encourages their consultants to learn new skills, we are rewarded for doing so. PW is the model for professional development. I love learning new things!

2) I love writing and teaching. At Pragmatic Works, I get to do everything I love to do.

1) And the number one reason I love working for Pragmatic Works: I love being a superstar and PW lets me be one!

Categories: SQL Server
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