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Category: Business Intelligence

What’s New In Power BI 2.0?

Meagan Longoria tells us what’s in Power BI version 2.0:

The Microsoft Power BI team was fast and furious in 2015, and there are no indications they are slowing down in 2016. If you haven’t checked out Power BI V2 since it was first released last summer, you might want to take another look. Many features have been added and updated since then. Based upon the release schedules since July, it seems there are 3 separate release cycles for Power BI:

  • The Power BI Service (PowerBI.com) gets weekly updates.

  • The Power BI Desktop tool gets monthly updates.

  • The Power BI mobile apps get monthly updates.

I expect no fewer than 6 updates per week from the Power BI team.

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Automating SSAS deployments

Matt Smith as introduced SQL Server Analysis Services deployments to Octopus Deploy:

The only thing missing was SSAS. After watching Chris Webb’s video tutorial –Cube Deployment, Processing and Admin on Project Botticelli, I decided it had to use Microsoft.AnalysisServices.Deployment.exe. After a bit of scripting and testing, I managed to write a PowerShell that updates the xml config files for the deployment – it sets the ProcessingOption to DoNotProcess’. It updates the Data source – where the cube will refresh the data from. The script isn’t perfect. For starters, what if you have more then one data source? Also what if your not using SQL Server 2014? Still the great thing about open source is that other can update it. Anyone can improve it, its not reliant on me having free time. So hopefully by the time we move to SQL 2016 someone will have already updated it to work with SQL 2016.

A big part of product maturation is automated deployment.  Good on Matt for introducing that to the community.

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What Is Business Intelligence?

Rolf Tesmer digs into the concept of BI:

Hunting the web for the general definition pulls up many one liners – and yes I guess everyone who is anyone will have a way to define it, and that definition is (or should) be based on their own experiences with building, deploying or supporting BI solutions.

If you are looking for a nice short collection of some of those definitions – and a further explanation of why you need BI – then this is a great post (http://www.jamesserra.com/archive/2013/03/why-you-need-business-intelligence/)

Rolf unpacks the definition and gives us some insight into the nature of Business Intelligence.

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Conformity In Self-Service BI

Paul Turley has a nice post on some of the risks of self-service BI:

In some solutions with a manageable scale and a reasonable tolerance for a certain amount of data loss and inconsistency, this approach may be just fine.  There are very good reasons for inconsistencies between sets of data that come from different sources.  When 20 million fact rows are obtained from an online ordering system and .1% don’t have matching customers records that come from the system used to manage the master customer list, it may simply be a question of timing.  Or, maybe the definition of “customer” is slightly different in the two systems.  Chances are that there are also legitimate data quality issues affecting a small percentage of these records.

Whatever the case may be, a data conformity or potential data quality issue in this design scenario falls on the model designer to either fix or ignore.  Again, this may or may not be a concern but it is a decision point and might raise the question of ownership when questions about data quality are raised.

Paul then goes on to show how this gets fixed in a traditional model and where you need to watch out with SSAS Tabular.  Good essay worth reading.

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Power Pivot Compression

There might be a theme to today’s posts…

Matt Allington shows us compression in Power Pivot:

Power Pivot would end up storing a table that looks more like the black table above (rather than the blue one), keeping just the minimum amount of information it needs to rebuild the real table of data on the fly when and if required.   If the black RLE table ended up taking more space than the original column of data, then there would be no benefit of RLE and the original column of data would be stored.  Power Pivot may use one or more of the other compression techniques used as well as, or instead of RLE – it all depends on the specifics of the actual data.

This is a very interesting look at ways the Power Pivot team optimize data storage.

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Fitbit BI

Reza Rad has a three-part series on applying BI tools (specifically, Power BI) to Fitbit.

Part 1:

So for this post we are going to build that dashboard (not all of that obviously, because we don’t have the data required for all of that), but most part of it with Power BI. You will see how easy and powerful is Power BI in this kind of scenarios, and you will see how you can be the BI Developer of Fitbit in a few steps of building this demo.

Part 2:

Unfortunately Power Query or let’s say Power BI doesn’t have a loop structure, and that is because of the functional structure of this language. However there are data structures such as Table and List that can be easily used with each singleton function to work exactly as a loop structure does. Here in this post I will get you through the process of looping into files in a directory and processing them all, and finally combining them into a large big table. You will also learn some Power Query M functions through this process.

Part 3:

Fitbit calculates based on my current weight and age (I assume) how much calories I have to spend each day. I don’t know that calculation, So I create a static measure with the value of 2989 for the amount of calories I have to spend each day. I also create StepsCap measure with 12000 value showing that I have to walk 12000 steps a day, and another one for FloorCap with the value of 10. I created a Calories HighEnd measure with 5000 calories as value (I will die if I burn more than that!). You can create all these measures easily in Data tab.

This is a nice combination of work and play, building an interesting system with a data set interesting to the author and freely available.

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