Callum

Callum Green's Blog

Direct Query vs Live Connection in Power BI – Part 2

This instalment of the Power BI blog series focuses on the two other differences between Direct Query and Live Connection in Power BI. 

If you haven’t done so already, be sure to check out Part 1, which concentrated on Quick Measures and Relationships.

Feature Differences

Find below a Power BI Direct Query screen, focusing on the final 2 differences (highlighted green):

o   New Hierarchy

o   Change to Import Mode

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New Hierarchy

Hierarchies are very useful with Power BI, especially when wanting to drill up and down within levels of data e.g. Day > Month > Year. Let’s try and create a New Hierarchy in Live Connection mode:

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New Hierarchy does not appear in the list of options.

This is because hierarchies are typically created within the OLAP Cube, therefore, it makes sense why the option is not available.  However, I would argue that Direct Query allows you create a hierarchy, so why can’t Live Connection?

Direct Query stores the hierarchies within a smaller Power BI model (Tabular Cube) running on a report developers local machine.  Ironically, Live Connection does exactly the same thing when you create an ad hoc measure in Power BI. It is surely a matter of time before hierarchies are also supported.

Change to Import Mode

Direct Query mode supports the ability to easily switch to Import Mode.  This is a useful option, especially for a self-serve analyst wanting to make transformations and shape the data.  By right clicking the highlighted option below, a simple wizard appears:

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When the import completes, the data (from tables in the database) will be stored in a Tabular cube on the local machine where Power BI Desktop is running.  If we navigate to the same area within Live Connection, notice there is no option to change to Import Mode (“click to change”:

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Once again, I am not really sure why this feature isn’t support in Live Connection mode.  I can understand why it would be more difficult to convert a Multidimensional Cube (MDX) into a local Tabular Cube (DAX), but if Live Connection already points to Tabular, it’s an exact copy. 

To vote for Import Mode functionality within Power BI Live connection, click here.

Coming Soon

Part 3 is the final instalment of the blog series, specifically focusing on the underlying Power BI Data Models in Direct Query and Live Connection.

Further Reading

Other than the Power BI Blog, there are some other great pages out there too:

o   Power BI Blog - http://bit.ly/2rkLGoq

o   Import Mode vs. Direct Query - http://bit.ly/2t4ragx

o   Direct Query in Power BI - http://bit.ly/2nUoLOG

o   Live Connection in Power BI – http://bit.ly/2tfJr5L  

Contact Me

If you have any questions or thoughts, please leave a comment below.  My Twitter details are also provided.

Twitter:                @CallumGAdatis

Direct Query vs. Live Connection in Power BI – Part 1

There are lots articles out there that compare Import Mode vs.Direct Query, but people rarely talk about if there are any differences between Direct Query and Live Connection.  “Wait. Aren’t they the same thing?”  Well, not quite.

The first big difference between Direct Query and Live Connection is the type of data source used for the connection.  The former uses a database connection (typically SQL Server), whilst the latter requires an Analysis Services OLAP Cube.

This blog series won’t explain what Direct Query and Live Connection can do (found here and here), but will instead highlight the other subtle differences between the two connections. 

Feature Differences

There aren’t any features I can find that are available in Live Connection and not Direct Query.  However, there are a few the other way around.

I will first show you a Power BI Direct Query screen, focusing on the 2 of the 4 differences (highlighted green):

o   Quick Measures

o   Relationships

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Quick Measures

This feature was first released in April 2017 and is available in Import Mode and Direct Query.  It enables a non-technical Business Analyst to create relatively complex DAX aggregations, with the use of a nice Wizard.  To access Quick Measures, right click on a measure or attribute and select Quick Measures.  Let’s try the same thing in Live Connection mode.

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You’ll notice that Quick Measures is missing from the list of options.

I find it bizarre that Live Connection doesn’t support Quick Measures, especially when using a Tabular Cube as the connection.  The Power BI DAX language and engine are the same as Tabular, so you would think the two are compatible!

Please vote for this feature to be added into Live Connection - http://bit.ly/2umsiJy.

Relationships

There are two tabs displayed on the left-hand pane in Direct Query mode.

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If you click the highlighted tab, it opens a Relationships page – where you can begin to join datasets (from the database) together.  I created a manual relationship that joined DimEmployee and DimDate together – as shown below.  No relationships are created in the underlying SQL Server database, but instead stored within the Power BI model.

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In Live connection, the left-hand pane looks bare:

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There is no option to create any form of Relationship against the Live Connection Tabular Cube.  This kind of makes sense because a BI Developer would be the person responsible for creating relationships within an OLAP Cube.  I would argue that if you want the ability to mashup data or create your own relationships, you shouldn’t be connecting to a Cube in the first place.

Coming Soon

Check out Part 2 of my blog series - available here.  The focus of this article are the Add Hierarchy and Change to Import Mode features.

Part 3 will conclude the Trilogy, where I go off-piste slightly and focus on the Data Models in Direct Query and Live Connection.

Further Reading

Other than the Power BI Blog, there are some other great pages out there too:

o  Power BI Blog - http://bit.ly/2rkLGoq

o   Import Mode vs. Direct Query - http://bit.ly/2t4ragx

o   Direct Query in Power BI - http://bit.ly/2nUoLOG

o   Live Connection in Power BI – http://bit.ly/2tfJr5L  

Contact Me

If you would have any questions or thoughts, please leave a comment below.  My Twitter details are also provided.

Twitter:                @CallumGAdatis

Coming Soon in Power BI - June 2017 & Beyond

Whilst I wasn’t fortunate enough to attend the Microsoft Data Summit on June 12-13 2017, I managed to view the keynote through a live webinar.  The majority of this session contained details of what is coming soon in Power BI, more than likely over the next few months.  We all know the ‘roadmap’ can be as long as a piece of string, but I am hopeful the features mentioned in this blog will all be available by the end of the calendar year.

Without further ado, I will move onto the features.

Out Now

The below features were actually released in the June Power BI Desktop Download.

ü  Data bars for new table & matrix (preview)

o   This is the concept of having spark lines within a table or matrix, providing trend analysis in a visual way. 

ü  Fonts in Visuals

o   You can now alter the font in a visual, the same as the functionality within a text box property.  These small configurations actually make a big difference.

Coming Soon

 

ü  New SKUs for power bi premium specifically targeting embedded workloads...  EM1 and EM2 SKUs starting at $625 per month.  The difference pricing tiers are:

 

ü  Embed using new SKU’s into SharePoint and MS Teams easily

o   I wander if the code will be completely free?

ü  New Visio diagram control – auto mapping to data model entities.  Copy and paste a Visio diagram into power BI and it hooks up the data elements.

o   This looked really slick in the Microsoft demo but previous experiences with this functionality un SharePoint was a little clunky and fiddly.

ü  Embed a Power APP into Power BI – with write back functionality 

o   This could replace MDS for certain scenarios, depending on the how complex your reference/master data is.   

ü  New Quick Measure gallery – a new DAX measure, upload it to the gallery and have your name referenced in the Product.

o   Chris Webb recently had this privilege, with a link to his blog series here.

ü  Better Custom Visual support.

o   New button to install custom visuals from store without having to download and import first.

ü  Ability to remove stop words in the Word Cloud Custom Visual

ü  Drill through to other report tabs using Page Settings.

o   This is huge for Power BI. Something seemingly so simple is not available but I for one am very excited for when this feature is released.

ü  Bookmark pane to facilitate a ‘save state’ (all current filter settings).  In addition, it will support analysts who want to tell a story or provide a walkthrough.

o   Show/hide visuals on a page when building story

ü  Create buttons

o   Turn a button into a report page link

o   Use buttons to show/hide visuals

o   Again, a simple, but MASSIVE feature for Power BI.  This really helps with the user’s reporting journey.

ü  Quick insights type functionality built into visuals and desktop to answer “why” questions -  using AI

ü  Waterfall chart improvements

ü  Cortana Improvements

o   More integration

o   Using colleague names in Cortana to find PBI workbooks

o   Conversational Q&A – refine answer with further questions

ü  What-If Analysis

o   Like Excel

ü  Annotation support for presenting back to customers. 

o   In effect, you can doodle or draw on top of a Power BI report.

Further Reading

I would advise subscribing to the Power BI Blog, in which you will hear of any new announcements to features, etc.  In addition, there are some other great pages out there too:

o   Power BI Blog - http://bit.ly/2rkLGoq

o   Power BI Premium Capacity White Paper - https://aka.ms/pbiewhitepaper  

o   Chris Webb Blog Page – https://blog.crossjoin.co.uk/

Contact Me

If you have any questions or thoughts, please leave a comment below.  My Twitter details are also provided.

Twitter:                @CallumGAdatis

Handling Web Page Errors in Power BI Query Editor

I was recently asked if it was possible to handle Error Rows in the Query Editor and more specifically, to web pages that do not exist from source. The user broached me with this question after following a previous blog on looping through multiple web pages in Query Editor – found here.

In my blog post, there were no errors from the loop output but in the user’s workbook, they were losing data directly below the expanded error row. My first suggestion was to use the Remove Errors UI option, which would delete the problematic row. However, they wanted a way of treating the errors as an Unknown Member and did not want to lose the data entirely

It is assumed consumers of this blog already have knowledge of the Power BI Query Editor and the UI functions used in the examples.  

Scenario

I created a new workbook that connects to local Council Facebook pages. Each page has a unique Object ID, which will be used as the parameter in the loop. The Council “Camberley” deliberately contains an invalid Object ID. I then proceeded to create a Parameter and Function, replicating the exact steps from my previous blog.

When I invoke the function (through the use of a Custom Column), the following is produced:

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As you can see, Camberley Council produces an error in the fnInvokeCouncils column. If we expand the contents (highlighted in yellow), the Facebook page data appears. Upon further inspection, the Farnham and Rushmoor council data are available, but Camberley (incorrect Object ID) and Guildford are not.

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The error message is a little misleading but let’s save the debugging debate for another day. The key observation is “Guildford” data is not available, simply because it comes after “Camberley” in the list. Whilst we want to see errors in a Query, we do not want them causing data loss.

Resolution

As I mentioned at the beginning of this article, using the Remove Errors function would prevent the loss of Guildford data. However, the user needs to handle errors as Unknown Members and conform to a typical Kimball Data Warehouse. 

I am sure there are many ways to fulfil the requirement, but here is how I approached it:

1.       Duplicate the existing ‘Councils’ query, naming it ‘Councils Error Rows’.

2.       Switch back to the ‘Councils’ query and Remove Errors, leaving only three records:

 

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3.       Expand the fnInvokeCouncils column, opening up the underlying fields and data:


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4.       In the ‘Council Error Rows’ query, apply the Replace Errors UI function - inserting the string “Validation Failed”.

5.       Add a Custom Column, writing the following M:

if [fnInvokeCouncils] = "Validation Failed" then 1 else 0

This is a simple IF statement that sets the error rows to 1.

6.       Now filter the selection to only display ErrorRows with the value of 1. This is achieved by using the Filter Rows UI function. The ‘Council Error Rows’ query now looks like the following:


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7.       The columns must match the ‘Councils’ query, meaning 4 new Custom Columns are needed. We can hardcode the values and remove any unwanted columns.

 

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8.       Right click on the previously modified ‘Councils’ query select Reference. Name the query ‘All Councils’. This makes it easier to track the transformations and persists any future changes made to the raw data.  

9.       Within the ‘All Council’ query, select Append Query transformation. Choose ‘Council Error Rows’ as the table to append and click OK.
 

10.   We are now left with a Union of both datasets, containing the Unknown Member and data from all other Councils.

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11.   The Unknown Member record is visible within the final Query.

 

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Summary

I have shown you how to get around two different problems with Error Rows in the Power BI Query Editor. The first is how to retain all correct data, whilst the second is keeping error records and inserting them back into the dataset as an Unknown Member. Both methods are dynamic, meaning that if we added a new Council called ‘Basingstoke’, we would see the underlying data regardless of whether the underlying Facebook Object ID exists or not.

Whilst none of the transformations in this blog are overly technical, there are quite a few hoops to jump through to retain all data from a web page/Facebook. Having said that, I am sure there are a few other ways people could approach this problem. I would be really interested to speak to anyone who does have an alternative solution.

Further Reading

Query Editor Basics (Power BI blog) – http://bit.ly/2pwBdo1
Unknown Members in Data Warehousing -
http://bit.ly/2qTefwe
Loop through Multiple Web Pages using Power Query - http://bit.ly/2q3a8Nc

Contact Me

If you would like a copy of the workbook containing the examples or want to know more about the Query Editor within Power BI, please leave a comment below. My Twitter details are also provided.

Twitter:  @CallumGAdatis

Slicer Properties in Power BI: Header or Title?

I recently shared a Power BI Report with a customer and they reported that the “Clear Selections” option (Eraser icon) was not available when they used the Slicer.  It took me a while to work out why this was.

This blog will illustrate how you can lose the “Clear Selections” functionality, depending on what Format settings are applied to a Slicer.  I will also show how to work around the formatting constraints, which will help prevent you from ever experiencing the issue.

In order to follow the examples, you will need access to Adventure Works 2014 SSAS Tabular and of course, Power BI Desktop.  Follow the appropriate links to download what you need.

Use Case

I have created a very simple report (available on request), using a Product Category Slicer and a Map to display Internet Total Units. 

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o   Format property.

o   Select All is off, ensuring multi select is allowed.

o   Header is off.

o   Title is on, used instead of Header and configured to look like the below:

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After multi-selecting the attributes in the Slicer, I tried to “Clear Selections” – which is normally available as an option like below:

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However, it does not appear in the report I created:

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The reason I cannot see the “Clear Selections” option is because I am not using a Header.  When I use this instead of Title, the Slicer contains the required feature.

Header vs Title

This made me wonder what other differences are there between Title and Header but in fact, there aren’t many.  The subtle differences to be aware are displayed and described below:

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o   Header can have an Outline, which includes the ability to underline text.

o   Header is constrained to displaying the name of the attribute (“Product Category Name”), whereas a Title can be customised (“Select Category”).  You can rename your source data attribute to get around this, however.

o   Title enables you align the text, but this is not possible with a Header.

o   Header contains the “Clear Selection” option.

Workarounds

There are couple of ways to work around the missing “Clear Selections” issue, which I will demonstrate below.


Option 1

I could simply switch from a Title to a Header, but then we would lose the ability to centre align the description.  Instead, we can set both options to ‘On’.

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After some formatting, the Slicer is pictured below.

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o   The Header text is set to White, therefore, it not visible. 

o   You can still see the “Clear Selections” option.

o   However, there is white space in between the Title and Header.  This not only wasted space, but also looks a bit strange from a visual perspective.

o   The actual sections themselves (e.g Bike) are a bit squashed and disproportionate to the Slicer border.

 

Option 2

The other workaround involves a little more work, but gives the impression that only a Title is being used, but with the added functionality of the “Clear Selections” option.  Furthermore, there is no longer the white, empty space.

 

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o   Two objects were used to produce the result:

·         Text Box

·         Slicer

o   The text box has been formatted and labelled with ‘Select Category’, using the Title property.  This allows you to change the font colour.

o   The Slicer is using the Header option, ensuring “Clear Selections” is available. Title is turned off to reduce the empty space.

 

Summary

So why are there two types of properties for Slicers and other objects like Text Boxes?  I am not entirely sure myself, but occasionally, you may want a Slicer to contain a Title with an Attribute Name as the Header underneath. This gives you a Title > Sub-Title concept (illustrated below).  I do understand why the “Clear Selections” feature is specific to the Header setting, as it directly relates to the Slicer attribute.  

There are certainly ways of solving this issue - here are just a couple of suggestions:

1.       Providing continuity across both the Header and Title format settings.  Quick fix, but not necessarily solving the ambiguity around both options.

2.       The Header setting is contained within the Title, meaning both sets of functionality are merged into one.  It would make the usability of a Slicer (especially from a development perspective) a lot better.

Whatever Microsoft decide to do in the future, I really hope they tidy up and fully define the Settings within Visuals, Slicer and Text Boxes.  Some things are confusing, especially to self-serve Business Analysts who rely on intuitive reporting tools.

Further Reading

Power BI Documentation –  http://bit.ly/2oKxy8V
Power BI Community Blog - http://bit.ly/2oU42OE
 

Contact Me

If you would like a copy of the workbook containing the examples or want to know more about any of the Power BI settings/properties not mentioned, please leave a comment below.  My Twitter details are also provided below.

Twitter:                                @CallumGAdatis

Themes in Power BI

Microsoft recently released Themes to a Preview version of Power BI.  The concept is pretty simple – imagine having standardized colours that can be applied to charts and matrix visuals? This is where Themes come in. The theory is this will ensure Power BI developers adhere to company policies and can do so quickly and efficiently. The reality is Themes are still lacking a lot of key configurables, but I will get onto that later.

Whilst this blog is not going to show you how to import or apply a Theme, it will demonstrate how to create one and what each configuration means. For a great high level walkthrough of the feature, click here. The aim is to not only show how this makes a developers life easier, but where it can actually be improved too.

JSON Configuration File

I have pasted the following code into Notepad++, which incorporates the Adatis branding and colour scheme.  For many developers, you will recognize the coding language – JSON (JavaScript Object Notation). If you would like to know more about JSON, check out Jason Lengstorf’s blog.

Use the below code as a template for your Theme:

{

  "name": "Adatis",

  "dataColors": [ "#002C5C", "#006FBA", "#81C341", "#F4AA00", "#003876", "#00448F", "#0051A9", "#007ED4","#008DED","#74B238","#8EC954","#DB9800","#FFB60F" ],

  "background": "#FFFFFF",

  "foreground": "#002C5C",

  "tableAccent": "#81C341"

}

Notice there are a number of ‘#’ within the code.  These are Hex numbers, in which Power BI natively uses to determine a shade of colour.  This is common across many reporting/image editing tools. 

Configurations Explained

I will now explain what each line of code means and how it will effect a simple un-themed report.

 

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1.       The name of the Theme.

2.       The colours that appear in a pre-set chart colour palette and the order of which a visual applies them e.g. Dark Blue first, Light Blue second, Green third, etc.

3.       Font colour for the matrix or table visual.

4.       Primary background colour for a matrix or table visual.  Even though the setting says’ ‘foreground’, it is more of a background colour!

5.       The table accent applies to a matrix or table visual, displaying as the grid outline or contrasting background colour, depending on what type matrix/table is applied.

 

Applying a Theme

I have created a quick Power BI report, which is using the default colour schemes applied.  The only formatting applied was to the Matrix visual and this was simply changing the style of the grid.  Here is how the report currently looks:

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Now let’s import and apply the ‘Adatis’ theme.  You will see some of the visuals have changed, whereas other elements of the report haven’t.  I will explain in greater detail.

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1.       The Report Title, Slicers and Titles are not affected by the Adatis theme.  This is because you cannot currently configure fonts in the JSON file.

2.       The matrix visual has updated, using the three configurations form the JSON file:

a.       background (White)

b.      foreground (Navy Blue)

c.       tableAccent (Green)

3.       The Pie and Bar chart have picked up the colours in the order they are specified in the JSON file, using the dataColors property.  However, note the colour of both data points on the Bar Chart – rather than use different colours, it uses the same primary blue colour.  Not ideal if you want to plot one colour for ‘Male’ and another for ‘Female’.

4.       The Waterfall chart hasn’t changed at all.  We would expect the Adatis colours to have been applied, but this visual seems to ignore the configuration.

We can change the Waterfall Chart manually, using the imported Adatis Colour Palette. 

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Here is how the chart now looks:

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What’s Next?

Whilst the concept of Themes is great, the current preview functionality is extremely limited and very much ‘Version 1’.  I am a little disappointed with how inconsistent the application of a theme is, which were highlighted in my points above.

With the majority of new Power BI features, Microsoft continue to improve the usability and functionality over time, so I am very hopeful more Theme properties will be opened up in the underlying JSON configuration file.  Options such as font colours/styles, consistent theme application (for all visuals) and company logos are all necessary for this to become really powerful.  There were talks of a CSS type of configuration in Power BI, but this has yet to announced or released.  Imagine how powerful and cool that would be?

As a BI Consultant, I am not currently comfortable with demoing Themes to a client, simply because of the clear gaps.  Once the feature is more mature and in GA, I think enterprise companies will really benefit from standardising reports across their business.  For now, Themes will remain a glorified colour palette.

Further Reading

o   Jason Lengstorf’s JSON Blog –  http://bit.ly/2aU1OHS

o   Power BI Report Themes BI Blog - http://bit.ly/2mPq69l

Contact Me

If you would like a copy of the workbook or have any questions about this blog, please leave a comment below.

Twitter:                                @CallumGAdatis

DAX Calculated Tables in Power BI

Calculated Tables have been around for a while in Power BI, but today I found a real life scenario for using them. I connected to Manchester United’s Facebook page and plugged the data into Microsoft’s Cognitive Services. In essence, I want to measure Sentiment Analysis and find out how many times a supporter has mentioned one of Manchester United’s rival football teams.

You are probably wondering what this has to do with a DAX Calculated Table, so let me explain.  I have pulled down the Facebook data (from the API), but when trying to undertake a GROUP BY for Likes, Loves, etc. in the Query Editor, Power BI hangs and the query never resolves.  Whilst I cannot pinpoint exactly why this happens, I would guess that the number of API calls to Facebook are exceeded and some form of timeout occurs.

This blog will walk you through how to create a DAX Calculated Table and apply a Group By to get the count of reaction types.  There are a number of articles already out there showing examples of a Calculated Table and I have provided the links at the bottom of the post.

Existing Query

Currently, my query looks like the below:

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The only remaining task is to apply a COUNT of all records, GROUPED BY Reactions.Type and id.  If I try and use the Query Editor functionality within the UI, the transformation step never completes. I am left with the following message in bottom right hnd side of the Query Editor:

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After waiting two hours for the GROUP BY query to resolve, I gave up.  The alternative is to use a DAX Calculated Table and I will show you how I achieved this:

Calculated Table

In order to create A Calculated Table, come out of the Query Editor, navigate to the Modeling tab and select New Table.

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Now we can write some DAX.  Pasting the below syntax into the new Table will achieve the Group By on the ‘Reaction Man United’ query.

ReactionTotalsManUnited = GROUPBY (  

ReactionsManUnited, ReactionsManUnited[id], ReactionsManUnited[reactions.type],  "TotalReactions", COUNTX( CURRENTGROUP(), ReactionsManUnited[reactions.type]) 

) 

Let me break down the code:

o   Calculated Table named as ‘ReactionTotalsManUnited’

o   GROUP BY function, grouping all reaction Id’s (‘id’) and types (‘reactions.type’)

o   COUNTX function applied over reaction type, using the CURRENTGROUP() function to ensure the unique count is made by Id and Type within the ‘ReactionsManUnited’ table.

Finally, to test the new DAX table works, I have created a basic KPI Card.  It is aggregating exactly expected.

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Conclusion

Let’s recap.  I have shown you how to use three DAX expressions, albeit nested together in one statement.  This demonstrates how powerful and flexible the language is.

o  GROUP BY

o  COUNTX

o  CURRENTGROUP

I made use of the Calculate Table functionality due to poor performing queries made to the Facebook API.  There are many other reasons for using them, with some good examples provided in Chris Webb’s blog. 

Where possible, you should always use Query Editor (and M language) for ad hoc transformations, although a DAX expression can sometimes get around slow performing queries.  DAX measures are evaluated at run time and in memory, whereas the Query Editor needs to pull down and refresh data after every applied step. 

I would strongly recommend that all budding Power BI developers learn DAX, in order to get the most out of your Power BI reports.  The Calculated Table function is just one of over 200 different expressions within Power BI.

Further Reading

o   Microsoft MSDN – http://bit.ly/2l34vsW   

o   Power BI Blog - http://bit.ly/2lBWRJc

o   Reza Rad’s Blog - http://bit.ly/2lBKjkW

o   Chris Webb’s blog - http://bit.ly/2m3IDlg

o   List of DAX Expressions (Paul Turley’s blog) - http://bit.ly/2mfBZ8y

Contact Me

If you would like a copy of the workbook or have any questions about this blog, please leave a comment below or contact me on Twitter (@CallumGAdatis ).

Dual KPI Custom Visual in Power BI

On February 8th, Power BI released a new custom visual called Dual KPI. The purpose of this chart is to visualise two measures over time and show their trend based on a joint timeline. The absolute values may use different scales e.g. Sales and Profit.

This blog will not only show you how to set up the new visual, but also demonstrate how changing some of the settings can enhance a report. Adam Saxton posted a YouTube video that also walks through the Dual KPI.

Pre Requisites

In order to follow my example, you will need a copy of AdventureWorksDW2014 database – found here. You will also need to download the following custom visuals:

o   Hierarchy Slicer – http://bit.ly/2kAv4Id

o   Dual KPI – http://bit.ly/2l1qCTp

NOTE:   This article assumes previous knowledge of downloading and importing Custom Visuals into Power BI Desktop. If this concept is new to you, Scott Murray’s blog gives great step by step instructions. 

Prepare Data

Open Power BI Desktop and Get Data. Point to the new AdventureWorksDW2014 database and drop down Advanced Options. Paste in the following T-SQL:

SELECT

        DPC.EnglishProductCategoryName

       ,DPS.EnglishProductSubCategoryName

       ,DP.EnglishProductName

       ,SUM([TotalProductCost]) AS [TotalProductCost]

       ,SUM([SalesAmount]) AS [SalesAmount]

       ,SUM([SalesAmount]) - SUM([TotalProductCost]) As ProfitAmount

       ,[ShipDate]

FROM [AdventureWorksDW2014].[dbo].[FactInternetSales] FI

INNER JOIN

       [dbo].[DimProduct] DP

       ON DP.ProductKey = FI.ProductKey

INNER JOIN

       [dbo].[DimProductSubcategory] DPS

       ON DPS.ProductSubcategoryKey = DP.ProductSubcategoryKey

INNER JOIN

       [dbo].[DimProductcategory] DPC

       ON DPS.ProductcategoryKey = DPC.ProductcategoryKey

WHERE ShipDate BETWEEN '2013-01-01' AND '2013-06-30'

GROUP BY

        DPC.EnglishProductCategoryName

       ,DPS.EnglishProductSubCategoryName

       ,DP.EnglishProductName

       ,[ShipDate]

When happy, click ‘OK’ to continue. The preview of the data will open.  Click Load, as we do not need to edit any data in the Query Editor.  Apply and changes and rename the query to ‘Internet Sales’ – final output below:

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Some measures and attributes need to be formatted within the ‘Modeling’ Tab.

o   ‘ShipDate’ = dd MMMM yyyy

o ProfitAmout’ = Currency

o   ‘SalesAmount’ = Currency

The final formatting step is to create a Product hierarchy, based on the three product attributes.  Navigate to the Data tab, right click on the ‘EnglishProductCategoryName’ attribute and select ‘New Hierarchy’.  Drag the attributes into the hierarchy and name it ‘Products’.  It should look like the following:

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Create Report Visual

We need to use both the Slicer and Dual KPI custom visual. To achieve this, follow the steps below:

Select the Hierarchy Slicer in the Visualizations menu and drag the ‘Products’ hierarchy on to the Fields box. The slicer will now appear in the report.

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Select the Dual KPI Slicer in the Visualizations menu and drag the following measures to the appropriate chart properties box:

a.       ‘ShipDate’ > Axis

b.      ‘SalesAmount’ > Top values

c.       ‘ProfitAmount’ > Bottom values

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The chart is now configured and each metric/visual is explained in more detail below. Only the top KPI (Sales Amount) is shown because both use the same calculations.

clip_image002[4]

  

1.       This is a fixed growth percentage, comparing the last (06/30/2013) vs. first (01/01/2013) data point on the graph. The metric acts as a static KPI.

2.       The Sales Amount value for the last data point on the graph. Also a static KPI.

3.       The data point currently being hovered over. This dynamically changes when you move along the axes.

4.       The Sales Amount value for the current data point being hovered over. Also dynamic.

5.       % since metric that looks at the Sales Amount for the last data point on the graph and works out the growth based on the current data point being hovered over. To use the example in the screenshot:

-          Sales Amount for 06/30/2013 = 51,596

-          Sales Amount for 05/17/2013 = 18,442

-          % since:  ((51,596 - 18,442) / 18,442) * 100 = 179.7%

Enhancing the Report

As with all custom visual in Power BI, there are lots of settings that you may never use. I have picked out some that enrich the capabilities of the Dual KPI Chart:

o   Fields

o   Warning State

§  Set alerts around data freshness and view warning messages.

o   Top/Bottom % change start date

§  For the fixed +/- % change on the chart, you can add an override start date. The dates could vary by product category and dynamically impact the % in the visual.

o   Format

o   Dual KPI Properties

§  Show abbreviated values, define multiple tooltips and show stale data warnings.

o   Dual KPI Chart Type

§  Choice of either Area or Line charts.

I have applied the Top/Bottom % change start date functionality and also formatted the chart properties. The report now looks a little more professional:

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Further Reading

o   Adam Saxton YouTube Video –  https://www.youtube.com/watch?v=821o0-eVBXo

o   Power BI Blog - http://bit.ly/2kudZ0a

Contact Me

If you would like a copy of the workbook or have any questions about this blog, please leave a comment below.

Twitter:  @CallumGAdatis

On-Premise Power BI: Part 2 – What’s next?

Part 2 of this blog series focuses on the future of Power BI On-Premise and what direction Microsoft are heading in.  This is a little tricky, as there haven’t been many formal announcements of how both SSRS and Power BI will work together from an architectural and pricing perspective.

I will be using this article as a forum to discuss both On-Premise and Cloud variations of Power BI, potential licensing and whether the new offering is actually just ‘Plugging Power BI reports into SSRS’.  The last statement is a little crude but from a business perspective, decision makers need to know what version of Power BI suits their needs.  We won’t get all of the answers right now, but it would be nice to shed some light on what appears to be a very dark room.

Cloud vs On-Premise

What does this mean for Power BI as a brand and more importantly, as a reporting tool?  The original purpose for Power BI was to offer self-service, ad hoc end user reporting.  However, as the product has matured, prospective clients have wanted it to do so much more.  “How can we incorporate Active Directory security”? “How do we share Dashboards within a specific workspace and limit permissions to it”? “But our business uses ultra-sensitive data and it MUST stay on a server in the UK…..”.  These are the types of comments/questions that get banded around a lot at the moment.

This is why Microsoft are offering both Cloud and On-Premise.  We are not yet in a position where definitive pros and cons can be laid out for each option, but this will soon be possible when Microsoft reveal their ultimate strategy.  There are still a lot on unanswered questions, especially around licensing.  A sensible assumption is that Power BI On-Premise will be covered under the typical Enterprise Edition version of SQL Server.  However, will there be an add-on fee for Power BI or will the general license costs go up?  What impact will this have on the Cloud costs?  There are some complicated pricing models for Office 365 users and the simpler £9.99 a month for a standalone Power BI Pro license.  Will the same pricing strategy exist or will On-Premise force Microsoft into a rethink?  I tend to stay away from speculation in my blogs and stick to facts, but I genuinely am interested to see how Microsoft market the variations of Power BI.

Coming Soon

After looking through various blogs and forums (links provided below), the following features/functionality will soon be available before Power BI On-Premise goes to GA.

  • Short-Term
    • Custom visuals
    • Additional data connectors (besides Analysis Services), cached data, and scheduled data refresh
    • Power BI mobile apps (viewing Power BI reports stored in SSRS)
  • Longer-Term
    • R Visuals
    • Support for integrating previous versions of SQL Server Databases (2008 +)  and Analysis Services (2012 SP1 +) with SSRS 2016
    • Support for all data connectors currently enabled for Power BI Cloud

Microsoft have also listed some Power BI cloud features that are not planned for the On-Premise version:

  • Dashboards – The concept of pinning a report and sharing it on an ad hoc basis
  • Q&A (Natural query language)
  • Quick Insights

Another pertinent question is “When will Power BI On-Premise actually be available in the real world?”.  Microsoft are targeting a production ready release for mid-2017, although nothing is official yet.  One thing is certain – it won’t be coming in a Service Pack or Cumulative Update.  Another big thing to consider is migrating from SSRS 2016 to SSRS with Power BI Reports, which Microsoft are promising will be easy.

Conclusion

At this stage, it is difficult to give any concrete information on where Power BI On-Premise is heading.  All we know is that Power BI and SSRS will be working together a lot more closely and the majority of functionality will be available in both. 

The concept of having Power BI reports shared and deployed to physical, on premise servers will accommodate companies worried about moving their data to the cloud.  As Power BI continues to increase in popularity, the overall security and infrastructure model will be scrutinized.  Cloud storage is often falsely labelled as a security risk, which is where the common corporate misconceptions are born.  It will be hard to change this train of thought, which is where the On-Premise Power BI offering will come in handy. Even more appealing is the natural integration with SharePoint and SRRS, enabling companies to use hybrid approaches, as well as not need to migrate old SSRS reports into Power BI.  Everything is managed in one location, thus reducing security risks and costs. 

Now we all sit tight and wait for Microsoft’s next big announcement.  If anyone has more information around Power BI On-Premise, please comment below.  

Further Reading

o   Power BI Reports in SSRS Release Notes - https://msdn.microsoft.com/en-us/library/4c2f20d7-a9f9-47e3-8dc3-c544a14457e0.aspx?f=255&MSPPError=-2147217396

o   October 2016 Technical Preview Blog – https://blogs.msdn.microsoft.com/sqlrsteamblog/2016/10/25/announcing-a-technical-preview-of-power-bi-reports-in-sql-server-reporting-services/

o   December 2016 Feedback Review Blog – https://blogs.msdn.microsoft.com/sqlrsteamblog/2016/12/16/power-bi-reports-in-sql-server-reporting-services-feedback-on-the-technical-preview/

o   January 2017 Technical Preview Blog – https://blogs.msdn.microsoft.com/sqlrsteamblog/2017/01/17/power-bi-reports-in-sql-server-reporting-services-january-2017-technical-preview-now-available/

Buffer() M Function in Query Editor (Power BI)

Whilst I have been aware of the Buffer() M function in the Query Editor of Power BI for a while, I had never really utilised its capabilities until now.  There are two main types of buffer functionality – Table.Buffer and List.Buffer.  To define them simply, Table.Buffer puts an entire table into memory and prevents change during evaluation, whereas List.Buffer provides a stable list, meaning it has some form of count or order.

This blog will focus on the theory behind the general Buffer() M functionality, picking a specific scenario of when it can outperform the standard Query Editor behavior.  I will also demonstrate that this is not always the most effective technique within the same scenario.  The article will not give you a hard and fast rule of when to use the Buffer() function, because it can depend on a number of factors.  These are described further below.

Note:    It is assumed you have existing knowledge of Query Folding and if not, one of my previous blogs should help greatly.

Scenario

I found inspiration from Chris Webb’s example, using the Adventure Works DW 2014 database – available here.

The requirements are:

1.       Obtain the first 10,000 rows from FactInternetSales

2.       Remove the majority of columns, retaining ONLY:

a.       SalesOrderLineNumber

b.      CustomerKey

c.       SalesAmount

3.       Rank the current row based on Sales Amount.

List.Buffer()

Assuming your database exists on a local server and is named AdventureWorksDW2014, copy the following code into the Advanced Editor in the Query Editor screen.

let

    //Connect to SQL Server

    Source = Sql.Database("localhost", "AdventureWorksDW2014"),

    //Get first 2000 rows from FactInternetSales

    dbo_FactInternetSales = Table.FirstN(

          Source{[Schema="dbo",Item="FactInternetSales"]}[Data],

          10000),

    //Remove unwanted columns

    RemoveColumns = Table.SelectColumns(

          dbo_FactInternetSales,

          {"SalesOrderLineNumber", "CustomerKey","SalesAmount"}),

    //Get sorted list of values from SalesAmount column

   RankValues = List.Sort(RemoveColumns[SalesAmount], Order.Descending),

    //Calculate ranks

    AddRankColumn = Table.AddColumn(RemoveColumns , "Rank",

          each List.PositionOf(RankValues,[SalesAmount])+1)

in

    AddRankColumn

You can visibly see the rows loading – one by one.  In total, it takes nearly 1 minute to load all off the results.

Now let’s use the List.Buffer() function in the RankValues step.

Replace:

= List.Sort(RemoveColumns[SalesAmount], Order.Descending)

With:

= List.Buffer(List.Sort(RemoveColumns[SalesAmount], Order.Descending))

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The entire transformation (from start to finish) completes in just under 2 seconds!  This is because the List.Buffer function stores the sorted values in memory and therefore, the rank calculation is only evaluated once.  The last query (and previous steps) were being evaluated multiple times.  The M language is both functional and at times, lazy.  In order to prevent the constant re-evaluation, buffer the list into memory. 

The final query output is shown below:

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Query Folding

We will implement the same requirements, but this time using Query Folding. 

The third step in our current transformation is called ‘Removed Columns’. This is what prevents Query Folding, as this function cannot be interpreted/translated to the native SQL Server T-SQL language.  All steps below are inadvertently not supported either. 

The way around this is to write SQL Server View (in SSMS) to import just the fields required from the underlying FactInternetSales Table.  The below query will give you the same result up to the ‘Remove Columns’ step.

CREATE VIEW dbo.VwFactInternetSalesAmount

AS

       SELECT SalesOrderNumber   

                     ,[CustomerKey]

                     ,[SalesOrderLineNumber]

                     ,[SalesAmount]

                     ,RANK() over( order by [SalesAmount] desc) AS [Rank]

       FROM   [AdventureWorksDW2014].[dbo].[FactInternetSales]

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The final steps are to filter on the top 10,000 rows and Group the Rows together – inserting the following M syntax into the last Applied Step:

let

    //Connect to SQL Server

    Source = Sql.Database(".", "AdventureWorksDW2014"),

    // Connect to SQL Server

    dbo_FactInternetSales = Source{[Schema="dbo",Item="VwFactInternetSalesAmount"]}[Data],

    #"Sorted Rows" = Table.Sort(dbo_FactInternetSales,{{"SalesOrderNumber", Order.Ascending}}),

    #"Kept First Rows" = Table.FirstN(#"Sorted Rows",10000),

    #"Grouped Rows" = Table.Group(#"Kept First Rows", {"CustomerKey", "SalesOrderLineNumber", "Rank"}, {{"TotalSalesAmount", each List.Sum([SalesAmount]), type number}})

in

    #"Grouped Rows"

 

The query now returns instantly (under 1 second).   Right click on the last applied step and select the View Native Query option, to show the underlying SQL.

select top 10000

    [rows].[CustomerKey] as [CustomerKey],

    [rows]. << Older posts Newer posts >>