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Power BI Blog

  • 4 new updates in Power Query

    Take Power Query to the next level: share your queries and create a corporate data catalog. Try Power BI for Free today!

    In this Post

    Another month is almost over and that means that a new Power Query update is due. We’re glad to announce the availability of the September 2014 Power Query update. Here is what is new:

    Download the Power Query Update

    Update summary

    Improved "Data Source Settings" Dialog

    Improved "Insert Index Column" transformation

    Additional options in Query Navigator in the Editor (reorder, delete, group)

    Option to disable Power Query Update notifications


    Update Summary

    The following features have been added or improved in this update:

    1. Improved “Data Source Settings” dialog
    2. Improved “Insert Index Column” transformation
    3. Additional options in Query Navigator in the Editor (reorder, delete, group)
    4. Option to disable Power Query Update notifications


    You can watch the following video or continue reading this blog post for more details about each feature.

    Improved “Data Source Settings” dialog

    With this update, we’re significantly improving the usability of the Data Source Settings dialog and addressing many of the functional gaps that it previously had.

    This dialog now allows users to:

    • Sort data sources by name/path or data source type.
    • Search within the list of data sources.
    • Select multiple items and delete them, or edit the Privacy Level for all of them at once.

    In addition to improving the initial dialog, we have also made the Edit dialog for each data source much more capable so that users can perform the following tasks:

    • Edit or delete stored credentials.
    • Control whether Power Query should encrypt connections to this data source or not.
    • Modify (or even set for the first time) the Privacy Level for the source.
    • For database sources, know whether any Native Query has been approved or not, and revoke all approvals.

    Improved “Insert Index Column” transformation


    We have added UX support for customizing new Index Columns in your queries. Before this update, the “Insert Index Column” option in the “Add Column” tab would always create a new index starting from 0. With this update, we’re adding the option to create a new index column starting from 1 or even to customize the starting value and row increment for the index.

    Additional options in Query Navigator inside the Editor


    In last month’s update, we added a Query Navigator inside the Query Editor to let users switch between queries without having to close and reopen the Query Editor. In this update, we’re extending the capabilities available within this Query Navigator to also let users reorder queries within the list, delete queries or create and manage their query groups.

    Option to disable Power Query Update notifications


    While we want everyone to be aware of and install new Power Query updates every month, we also understand that some folks may not want to be reminded with a system tray notification every time that a new update is available. We have added in this update an option to disable these notifications in the Options dialog. This option can be also specified as an installer command line argument: PowerQuery.msi DISABLE_UPDATE_NOTIFICATION=1


    That’s all for this update… We hope that you enjoy these features and continue sending us your valuable feedback and suggestions, so that we can continue improving Power Query with every new update.

    Download Power Query from our official download page.

    #update#power query#Power BI#Excel#Microsoft BI#data sources#share query#feedback#notifications#index column#query navigation

    Tue, 30 Sep 2014 16:00:00 GMT byMiguel.Llopis0 Comment

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  • Retail Location Analytics using Power BI

    This blog post was authored by Shish Shridhar, Director of Business Development - Retail, Microsoft

    By combining demographic data like Median Income, Education Levels, Median Age and customer purchasing data such as preferences, past purchases, and online behavioral data, retailers gain a more in-depth understanding of customer needs and wants than with just past purchase data. Power BI provides powerful capabilities for combining data from various sources and enabling visual correlation: you can certainly use the Data Analysis Tookpak in Excel and run a Correlation Coefficient on the combined data as well.

    To test this out, I came with the assumption that Seattle has the most Starbucks and that demographics affect the number of stores. To learn what the answer was, I used Power BI and Excel. Here is what I did:

    I looked for oData sources relevant to retail and found one via Socrata: https://opendata.socrata.com/


    I ran a search for Starbucks, to see if there was a list of Starbucks Restaurants from around the world. I did find a oData source of with a listing of all the Starbucks Restaurants around the world: https://opendata.socrata.com/Business/All-Starbucks-Locations-in-the-World/xy4y-c4mk


    Here is the oData source link to access the data: http://opendata.socrata.com/OData.svc/xy4y-c4mk

    Using Power Query for Excel, I was able to access the data using the oData option. This returns 20, 621 rows of data containing details of Starbucks restaurants around the world:


    To get a better insights from the data, I used Power View for Excel to create a visualizations. A quick drag & drop of the Brands against the count of the StoreIds showed me the brands represented by the data:


    I was curious about the countries with the most Starbucks, so I dragged in the Country information along with a count of the StoreIds. Here is the result:


    And interestingly, Seattle is not the city with the most Starbucks, as I’d assumed:


    Power Map for Excel enables visualizing this data on a Map as a layer of information:

    I was able to obtain US Census Data from Neustar and I imported this data into Excel. This data included Zip codes as well as detailed information about every Zip code. I could potentially use this information to correlate things like median age, median income, population around each of the Starbucks in the US. The Data looks like this:


    When I overlay the Census Data on top of the Starbucks Store Locations, I get a visual correlation between demographics data and Starbucks locations:


    Here is a Power Map for Excel video of two layers: Starbucks store locations with Median Income by Zip code:

    There are several sources of interesting public data that you can use to analyze Retailers: proximity analysis of retailers and their competition using data from Yelp and Foursquare; Correlating retail yelp rating and FourSquare CheckIns against Demographic data; Correlating Weather data against store performance.

    Here's the actual live visualization I created with Power View:

    You can check out some more examples at my blog.

    #power query#Power BI#Power Map#Power View#Power BI Training#Excel#live demo#data sources#visualization#Starbucks#store#zip code#retail#location#geo location

    Wed, 24 Sep 2014 16:00:00 GMT byPower BI Team2 Comments

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