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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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Creating Analysis Services Partitions Using BIDS

  • 10 April 2011
  • Author: briankmcdonald
  • Number of views: 32441

As your database grows in size, Analysis Services cubes that use that database grow along with it. As such, one thing that can improve performance of your cube is partitioning (splitting up) your measures. In this post, I am going to quickly show you how to switch from a table binding partition (default) to a query binding partition using a cube that I have built off of the AdventureWorksDW2008R2 database. In the end, you’ll know how to split your large measure groups into smaller chunks of data based on the year. 


When you have your cube opened, navigate to the Partitions tab. A screenshot of what this tab looks like is shown in figure 1. You can have a partition set up for the entire table (not split up) or you can write queries that would return the data you want to be included in that partition.


Figure 1: Partitions Tab

Partitions Tab  


For my example, I am going to break out my Internet Sales measure by years. Which for the AdventureWorksDW2008R2.dbo.FactInternetSales table, we have 2005 – 2008. So, I’m going to create four partitions starting with 2005. To switch the default table binding partition to query mode, select “Query Binding” binding type as shown in figure 2 below.


Figure 2: Switching the Binding Type

Switch Binding Type  


After you switch it to query binding, you’ll be shown a query that you want this partition to contain. Since the first set of Internet Sales records were in 2005, I am just going to update the script to contain “WHERE OrderDateKey <= 20051231” as shown in figure 3.


Figure 3: Update the Script

Update the Query 


After modifying the name assigned to my new partition to “Fact Internet Sales 2005” and choosing to design aggregations later, I now have the results shown in figure 4.


Figure 4: Partition Created for 2005

Partition 2005


Now, I need to click on the “New Partition…” link to create my other partitions in a similar fashion. The slimmed down scripts used to create each of these partitions are shown in script 1.


Script 1: Queries to Partition by Year


--2005 Internet Sales Partition Query


FROM [dbo].[FactInternetSales]

WHERE OrderDateKey <= '20051231'


--2006 Internet Sales Partition Query


FROM [dbo].[FactInternetSales]

WHERE OrderDateKey >= '20060101' AND OrderDateKey <= '20061231'


--2007 Internet Sales Partition Query


FROM [dbo].[FactInternetSales]

WHERE OrderDateKey >= '20070101' AND OrderDateKey <= '20071231'


--2008 Internet Sales Partition Query


FROM [dbo].[FactInternetSales]

WHERE OrderDateKey >= '20080101' AND OrderDateKey <= '20081231'


The results after creating all of these partitions should look like that shown in figure 5 below.


Figure 5: All Yearly Partitions Created

After All Partitions are Created


That’s all there is to creating partitions. So, after assigning these partitions to my AggregationDesign as shown in the example above, deploying and reprocessing, accessing the data within the partitions should be much quicker than having to search through a massive table containing hundreds of millions of records.


I hope that you have enjoyed this post. If you did, please take just a moment to rate it below! Also, if you don’t already, please be sure to follow me on twitter at @briankmcdonald. Also note that you can subscribe to an RSS feed of my blogs.



Brian K. McDonald, MCDBA, MCSD
Business Intelligence Consultant – Pragmatic Works

Email: | Blogs: SQLBIGeek | SQLServerCentral | BIDN

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