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Power BI and Big Data

If you’re worked in the wide and diverse field of information technology for almost any amount of time, it probably hasn’t taken you long to discover that the one thing constant about IT is that the technologies and strategies involved change faster than you can learn them. And if you work in business intelligence like I do, you don’t have to look very far at all to see change. The Microsoft Power BI team rolls out a software update every month! If I want to stay learned up on the technology, I have to really be on top of things.

About ten years ago when Hadoop was first being developed at Yahoo, I don’t think anyone could have anticipated the size of the ripples (more likes cannonball sized splashes) being able to access Big Data could and would have on the IT industry. Hadoop (and other advances in hardware and software technologies) gave us something we never had before: The ability to access and report on data in real time on a scale never previously imagined gives an organization to identify and understand trends and patterns in the data and gain previously unknown insights. The organizations that are able to leverage big data will be the organizations that leave their competition in the dust.

Set Up and Configure the Hortonworks Sandbox in Azure

Not only does Power BI Desktop give us the ability to connect to Hadoop Distributed File System (HDFS) for reporting we can also mash it up with other more traditional and structured data sources with minimal effort required. But that’s not what this blog post is all about. This post is about setting up a virtual machine in Azure running Hadoop and connecting to our Hortonworks Sandbox with Power BI Desktop :).

The first thing you do if you don’t have access to a Hadoop cluster is to set up the Hortonworks Sandbox on Azure. The good news is its free (for the duration of the trial) and its super easy. Just follow the instructions at this link to set up the Hortonworks Sandbox.

Hadoop in Azure

Once that’s set up, you’ll need to add mapping for the IP address and host name to your hosts file. Devin Knight has a blog on this that you’ll find helpful.

Connecting to Hadoop with Power BI Desktop

Once your Hortonworks Sandbox is set up, you’re ready to set up your connection to Hadoop with Power BI Query. Start up the Power BI Desktop and click Get Data. Scroll down and select Hadoop File (HDFS) and click Connect.

Get Data with Power BI

From there you can follow the rest of the wizard to load the data into the semantic model.

Load Data with Power BI

Once the data is loaded, you’ll need to modify the query to navigate to the data you wish to use in your model.

In Power BI Desktop, go to the Home ribbon and click Edit Queries.

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Three Best Practices for Power BI

Since the release of Power BI Desktop this past week, I’ve been really spending my extra time digging into the application focusing on learning and experimenting as much as I can. When my wife has been watching Law and Order: SVU reruns at night after the rug rats are in bed, I’ve been right there next to her designing Power BI dashboards like the total data nerd that I am. When my kids have been taking their naps during the weekend, I’ve been writing calculations in the model for my test dashboards. Or when I’ve been riding in the car back and forth to work I’ve been thinking of new things to do with Power BI Desktop.

Since I’ve been spending a decent amount of time with Power BI Desktop, I thought I’d take a moment to share three things to know and remember when designing your Power BI models and dashboards that I think will help you make the most of this tool and be effective at providing the data your business needs to succeed.

1. Optimize your Power BI Semantic Model

It probably hasn’t taken you long to figure this one out if you’ve built Power Pivot/Tabular models or at least it won’t when you do start developing Power BI dashboards. The visualizations in Power BI and Power View are heavily meta-data driven which means that column names, table or query names, formatting and more are surfaced to the user in the dashboard. So if you using a really whacky naming convention in your data warehouse for your tables like “dim_Product_scd2_v2” and the column names aren’t much better, these naming conventions are going to be shown to the users in the report visualizations and field list.

For example, take a look at the following report.

Power BI Dashboard without formatting

Notice anything wonky about it? Check the field names, report titles and number formatting. Not very pretty, is it? Now take a look at this report.

Power BI Dashboard with formatting

See the difference a little cleaned up metadata makes? All I did was spend a few minutes giving the fields user-friendly name and formatting the data types. This obviously makes a huge difference in the way the dashboard appears to the users. By the way, I should get into the movie production business. ;)

My point is that the names of columns, formatting, data types, data categories and relationships are all super important to creating clean, meaningful and user friendly dashboards. The importance of a well-defined semantic model cannot be understated in my opinion. A good rule of thumb is to spend 80% to 90% of your time on the data model (besides, designing the reports is the easy part).

I’d also like the mention the importance of the relationships between the objects in the semantic model. Chance are you will have a small group of power users that will want to design their own dashboards to meet their job’s requirements and that’s one of the beauties of Power BI. But when users began developing reports, they may query your model in unexpected ways that will generate unexpected behaviors and results. I only want to mention this because the relationships between the objects in the model will impact the results your users will see in their reports. Double check your relationships and ensure that they are correct, especially after you add new objects to the model since the

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Power BI Fantasy Football Player Stats Dashboards for Download

Every year at Pragmatic Works some coworkers, including consultants, marketing staff, support team members, software development staff and project management, partake in a company fantasy football league. And with the recent release of the new Power BI Desktop, I thought what better way is there to prepare to completely annihilate my coworkers and friends in an imaginary nonsensical game than by creating some nifty Power BI dashboards based on last years player stats as recorded by Yahoo! Sports. So I thought I’d walk you through some of the steps I followed to leverage the Yahoo! Sports NFL player stats page as a data source and some of the query transformations I applied to prepare the data for reporting.

Power BI dashboard with Power BI Desktop

Click here to download my Fantasy Football Dashboards Power BI .pbix file.

If you’re completed new to Power BI Desktop I highly suggest you watch my video walkthrough of Power BI Desktop or read my blog post which walks you through each step of creating your first Power BI dashboards with Power BI Desktop. Last Friday, I also blogged about my three best practices for designing a killer Power BI solution, so take a look at that.

To create these dashboards, I simply navigated to the Yahoo! Sports NFL stats page and found the page for each position I’m interested in for this fantasy football season. I copied the URL to my clipboard. In Power BI Desktop, click Get Data and then use the Web data source option. Then all you have to do is copy and paste the URL into the text box and click OK.

Get data from web with Power BI Desktop

Then select the HTML table that contains your data and click Edit. We need to edit our query because there are some issues with the data. By clicking Edit, we can apply transformations to our query which will allow us to do things like rename columns, remove unwanted columns, modify data types, create custom columns and much more.

Get data from web with Power BI Desktop

One thing you’ll notice in the above screen grab is that the column names are in the first row, so we need to fix that.

On the Home ribbon of the Query Editor, just click the Use First Row As Headers button. Pre

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Time Intelligence Filters in PerformancePoint 2010

  • 10 September 2012
  • Author: Mike Milligan
  • Number of views: 17541
  • 0 Comments

The time intelligence filters provided with PerformancePoint 2010 gives developers an easy way to provide users with a method for specifying time periods using common English terms such as "Last 6 months", "Same Period Last Year", "Rolling 3 months", and so on. These filters can be linked inside your dashboard to control Excel Services reports, SSRS reports, scorecards, and analytic girds and charts. Behind the filters are formulas based on the Simple Time Period Specification (STPS.)

In the text that follows, I hope to demonstrate the use of these concepts:

  1. Setting up and using Time Intelligence with both tabular and multi-dimensional data sources
  2. Using Time Intelligence with KPIs and Scorecards
  3. Using Time Intelligence with Analytic Grids, Charts, Excel Services reports, and SSRS reports
  4. Using both types of Time Intelligence Filters, the standard time intelligence filter and the connection formula
  5. Using the TI Connection formula to provide users with a From Date To Date range functionality

I have modified AdventureWorksDW2008 relational database by adding three views. One to increase our date dimension, one to extrapolate data to the current date, and one to use as a tabular data source. I made some pretty massive changes to the Adventure Works cube to simplify my demonstration process.

You can download those views and the XMLA for the altered SSAS database, here.

Once the cube was processed I had accomplished two things:

  1. My date dimension starts at the beginning of the year (best practice recommendation for working with time intelligence in PerformancePoint; but, not required.)
  2. My fact table has data through the current date.

In order for the time intelligence formulas to work properly, certain things must be set up on the data source connection. We will create two data connections for these examples. One will be a multi-dimensional data source to our cube and the other will be a tabular data source to a view that combines the data I need from the relational database. The process to add the time intelligence to these two data sources is similar; but, different.

Open PerformancePoint and create a new data connection by right clicking the data connection folder in the workspace browser and selecting "New Data Source." Select tabular list and SQL Server table. In the table field select the view included in the project files above named "vw_InternetSalesTabularExample." Select the time tab and select the options checked below in the screenshot.

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Next, create the data connection to the OLAP cube. Right click the data connections folder, select "New data source"; but, this time select the "Multidimensional" tab and select "Analysis Services." Below is a screenshot demonstrating what the time tab should look like once complete.

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Now that we have both data connections set up, I will first demonstrate using the time intelligence with a tabular source. The tabular data source can only be used with filters, KPIs and scorecards. Right click your PerformancePoint content list in the workspace browser and select "New", "KPI." Name the KPI "Internet Sales."

Within the KPI, rename Actual to MTD and rename Target to MTDLY. Click the data mappings for MTD, change the source, select "SalesAmount" in the measure dropdown and click the "New Time Intelligence Filter" button. Enter the following formula into the dialog:

Year.FirstMonth.FirstDay:Year.Month.Day

Now, change the data mappings for MTDLY, change the source, again select "SalesAmount" in the measure drop down, click the "New Time Intelligence Filter" button and enter the following formula into the dialog:

(Year-1).FirstMonth.FirstDay:(Year-1).Month.Day

Now, right click your PP content list folder in the workspace browser and select "New", "Scorecard." In the scorecard template dialog, select the "Tabular" tab and "SQL Server Table." Click Ok and the wizard will walk you through the next steps. Select your tabular data source, click next. Click the Select KPI button and choose the Internet Sales KPI we just created. Click Next a couple of times and the click Finish.

Delete the MTD column by right clicking on it. Then right click the MTDLY and select Metric Settings. Rename it to MTDLY vs MTD and select Actual in the "Additional data value" drop down. Click Ok. Drag ProductSubCat above "MTDLY vs MTD." Drag "Order Country" as the parent of the Internet Sales KPI. Click the Edit tab and then the update button. Your screen should look like this:

image_thumb7

To create a scorecard using the multidimensional data source; the steps would be pretty much identical.

For the next demonstration we will create a dashboard with an Analytic Grid that uses a standard time intelligence filter. Start out by creating an analytic grid using the SSAS data source that looks like the one in the screenshot below.

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Notice the "Date Calendar" is in the background of the grid. Now create a new time intelligence filter using the SSAS data source. We will enter the following Formula/Display Name combinations by clicking the Add Formula button for each one.

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Clicking the preview button will show the MDX behind the formulas.

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Note: The second row says no results because I ran this on Feb 29th, 2012. There was not a Feb 29th in 2011.

Notice the difference between the monthtodate, yeartodate, quartertodate, and fullmonth formulas and their SSRS compatible counterparts. SSRS can not handle these formulas so I used an alternate syntax to demonstrate how to accomplish the same thing using an alternate syntax.

Now create a new dashboard and drag the Analytic Grid to the design surface. Then drag the TI standard filter to the dashboard and connect it to the grid by selecting Member Unique Name from the TI filter and dropping it onto the drop zone in the analytic grid space. When the connection dialog comes up, make sure you select "Date Calendar" in the "Connect to" drop down.

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Deploy your dashboard and test out the filter.

Now, we will create a TI Connection Formula Filter. Right click in your workspace browser and select New, Filter; but, this time select the time intelligence connection formula filter. Add your OLAP data source and click Next and Finish. Next create a new dashboard or a new page on your existing dashboard. We'll use the same analytic grid; but, this time hook up the TI connection formula filter to dashboard. Everything is the same as the screenshot above except this time click the "Connection formula" button and enter the following into the dialog:

Quarter-6:Quarter-3

This is saying, calculate from the selected date 6 quarters back (1.5 years) and aggregate from there to three quarters out. So if today is 1/1/2012, 6 quarters back would be 7/1/2010 (1.5 years away). That is our start range.

3 quarters back from 1/1/2012 would be 4/1/2011.

Deploy the new dashboard, open SQL Server Management Studio's cube browser and verify the results.

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Dashboard

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SSMS

Note: select DATEADD(quarter,-6,'1/1/2012'), DATEADD(quarter,-3,'1/1/2012') returns 7/1/2010 and 4/1/2011. So why did I filter from 7/1/2010 to 6/30/2011 above? Because we are working in quarters. 4/1/2011 starts a new quarter and we include the entire quarter in the results. If this doesn't make sense to you, try an example using month-6:month-3 instead.

Next I will demonstrate how to provide your users with a From Date parameter and a To Date parameter to provide range based queries using the PerformancePoint Reports. You can provide similar functionality by using a single date parameter and using the Multi-Select tree display method; however, you and/or your users may prefer to use range based parameters.

The first step is to copy your existing Analytic Grid and give it a new name. We will work from this copy. Open this copy and click on the Query tab. Locate this line of code and position your cursor just before date.calendar part.

WHERE ( [Measures].[Internet Sales Amount], [Date].[Calendar].DEFAULTMEMBER )

Type FromDate in the parameter text box and click the insert button. Create another parameter in the same fashion by typing ToDate in the textbox and clicking insert. Modify the where clause so it looks like this:

WHERE ( [Measures].[Internet Sales Amount],HIERARCHIZE({<>:<>}) )

Create two new filters, one called TI From Date, and another called TI To Date using the time intelligence connection formula filter.

Now create a new page in your dashboard, drag the copied analytic grid to the design surface and then the two TI connection formula filters. Connect both filters to the analytic chart making sure to connect them to the proper parameters. Use Day:Day in both connection formulas. Publish your dashboard and test.

Next we will create a new report in PerformancePoint that connects to a SSRS report with a date calendar parameter. We can create two dashboard pages to demonstrate this functionality. One using the standard TI filter, and one using the TI connection formula filter. The only caveat you need to be aware of is that the members in the standard TI filter that use the ...toDate or ...Full(Month/Quarter) syntax will not work. You will have to use the alternate syntax describe earlier to get that same functionality.

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The YTD (Non SSRS) filter produces the error:

  • Default value or value provided for the report parameter 'DateCalendar' is not a valid value. (rsInvalidReportParameter)

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Note: These numbers match the earlier example we did using the Analytic Grid.

Next we will hook the TI filters up to an Excel Services report. The only issue I had when preparing this demonstration was an error that occurred when previewing the dashboard. "Attempted request on an invalid state. Unable to perform the operation" Google to the rescue!

Basically, I had to uncheck the box in SharePoint Central Administration for my Excel Services application that says 'Refresh warning enabled.'

I also received an error when I used the YTD (Non SSRS) filter with Excel Services.

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'An error occurred while attempting to set one or more parameters in this workbook. As a result, none of the parameters have been set. Click Ok to return to the workbook.'

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TI Standard Filter with Excel Services report

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TI connection formula filter with Excel Services report. Numbers match previous examples.

It should be noted that the 'Show Details' and 'Additional Actions' features are greyed out when using the TI filters with a date dimension in the background of the analytic grid and chart. One (not very good) workaround is to put the date dimension on the rows or columns to get this functionality back. The reason this work around is not very good is that your report does not look the same.

Example using analytic grid:

Dashboard page looks like this:image_thumb47

Right clicking a cell has 'Show Details' and 'Additional Actions' grayed out.

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I created a copy of the original and dragged the date calendar from the background to the rows underneath the geography to demonstrate what it would take to get the 'Show Details' functionality back.

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Not too bad using the YTD (Non SSRS) filter. (Not too good either...)

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But, change it to last 10 days, and it becomes very ugly, very fast.

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Moving the date calendar above the Geo in the rows helps a bit in some cases.

So nutshell, there are some caveats with working with the time intelligence features in PerformancePoint. Overall, they are a great feature to add some great functionality to your dashboard with minimal effort.

Miscellaneous Facts

Suppose you have a data source that has multiple time dimensions and you want to
use both time dimensions. The solution is to create a new data source for each time
dimension you want to use in your PPS solution. For example, if the cube you are using
has both calendar year and fiscal year dimensions, you can create two data sources
using the same server and cube information with the only difference being the time
dimension selected in the Time tab of each data source. When creating KPIs or filters,
select the data source with the time dimension that makes sense for that object.

Colon (:)
The colon is used to indicate a range of dates. For example, the statement Day-
1:Day-7 selects all the days between yesterday and a week ago inclusively.
Comma
(,)
The comma is used to combine two members. For example, the statement Day-
1,Day-7 selects today and a week ago today as distinct dates.

You can create two kinds of Time Intelligence dashboard filters:
1. Time Intelligence dashboard filters that include a list of dynamic time periods that you specify
2. Time Intelligence Connection Formula dashboard filters that use a calendar control to specify information as of a particular date. When you create a Time Intelligence Connection Formula dashboard filter, you do not specify a formula until you connect that filter to a report or a scorecard.

Periods-To-Date
Periods-to-date are a NEW type of TI formula added in Office 14. The result of a to-date period is an
aggregation of all time periods to date up to the last completed full period. Incomplete time periods are
automatically excluded. They are evaluated to the lowest degree of granularity in the data source by default. For example, if most granular time period in the data source were days, then the month to date expression will
aggregate all days from the beginning of the month to the last completed full day in the month. (The opposite is true for standard time periods They automatically include incomplete periods

Periods to date are not compatible w/ SSRS (personal experience.)

 

Here are some links that helped me put this blog post together.

PerformancePoint Relative Date Time Intelligence with Current Date Time

How to use Time Intelligence Filters with Excel Services or How to Pass a Range Selection into your Excel Report

From Date To Date in PerformancePoint Analytical Chart

Time Intelligence Post Formula Filter Template in PerformancePoint Server

PerformancePoint Time Intelligence - BI for the Masses

Create a Time Intelligence Filter by Using Dashboard Designer

Time Intelligence Differences Between Grids and Scorecards

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