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Category: Power BI

HDInsight + Power BI + Spark

Published 2016-02-02 by Kevin Feasel

Reza Rad has a nice walkthrough on integrating several powerful technologies:

Power BI can connect to many data sources as you know, and Spark on Azure HDInsight is one of them. In area of working with Big Data applications you would probably hear names such as Hadoop, HDInsight, Spark, Storm, Data Lake and many other names. Spark and Hadoop are both frameworks to work with big data, they have some differences though. In this post I’ll show you how you can use Power BI (either Power BI Desktop or Power BI website) to connect to a sample of Spark that we built on an Azure HDInsight service. by completing this section you will be able to create simple spark on Azure HDInsight, and run few Python scripts from Jupyter on it to load a sample table into Spark, and finally use Power BI to connect to Spark server, load, and visualize the data.

If you’re totally unfamiliar with Spark but interested in data processing, now’s a good time to start digging into the topic.

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What Is Power BI?

Published 2016-01-28 by Kevin Feasel

Angela Henry gives a high-level overview of Power BI:

There are lots of reasons to use Power BI, other than, it’s so cool.  For instance, Power BI makes it easy to see, in one glance, all the information needed to make decisions.  It also allows you to monitor the most important information about your business.  Power BI makes collaboration easy and when I say easy I mean EZ!  You can also create customized Dashboards tailored to those C-Suite folks or make a completely different dashboard based on the same data for those that actually do the work.

I’m personally astounded at how far visualization tools have come in half a decade.

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Power BI Gateways

Published 2016-01-21 by Kevin Feasel

Ginger Grant talks about Personal and Enterprise Power BI Gateways:

The Personal Gateway takes the data and imports it into Power BI. If you want to extract data from a variety of different places such as an Oracle Database, and Excel Spreadsheets, the Personal Gateway will support this, and the Enterprise Gateway won’t.   Remember the Enterprise Gateway only connects to three different data sources, and Excel and Oracle are not on that list. If you want to manage connection and refresh of the data as the administrator or provide access to the data to everyone who needs it, use the Personal Gateway.

It sounds like these are currently different enough that “both” might be the correct option within an organization, at least until Enterprise Gateway adds the missing features.

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What’s New In Power BI 2.0?

Published 2016-01-20 by Kevin Feasel

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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Power BI Matrices

Published 2016-01-11 by Kevin Feasel

Meagan Longoria wants to stack groups of measures in Power BI:

To my surprise, Power BI only lets you put multiple values on columns in a matrix. You can’t stack metrics vertically. Note: this is true as of 8 Jan 2016 but may change in the future. If you agree that this should be a feature in Power BI, please make your voice heard and vote for this idea on the Power BI forum and encourage others to vote for it as well.

The answer is a little complex.  Considering how frequently Power BI gets updated, hopefully they’ll make this a bit easier in the near future.

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Visualizing R In Power BI (Too)

Published 2016-01-01 by Kevin Feasel

Dustin Ryan is also looking at R visualization in Power BI:

Not only can we create and download custom visuals from PowerBI.com to extend the capabilities of Power BI, we can use R to create a ridiculous amount of powerful visualizations. If you can get the data into Power BI, you can use R to perform interesting statistical analysis and create some pretty cool, interactive visuals.

Dustin and Jan Mulkens are working on similar posts at the same time, so watch both of them.

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Power BI And R

Published 2015-12-31 by Kevin Feasel

Jan Mulkens has started a series on combining Power BI and R.

Part 1:

Fact is, R is here to stay. Even Microsoft has integrated R with SQL Server 2016 and it has made R scripting possible in it’s great Azure Machine Learning service.
So it was only a matter of time before we were going to see R integrated in Power BI.

Part 2:

From the previous point, it seems that R is just running in the background and that most of the functionality can be used.

Testing some basic functionality like importing and transforming data in the R visual worked fine.
I haven’t tried any predictive modelling yet but I assume that will just work as well.

Part 3:

So instead of printing “Hello world” to the screen, we’ll use a simple graph to say hello to the world.

First we need some data, Power BI enables us to enter some data in a familiar Excel style.
Just select “Enter Data” and start bashing out some data.

I’m looking forward to the rest of the series.

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Sentiment Analysis

Published 2015-12-24 by Kevin Feasel

Dustin Ryan and Patrick Leblanc used Azure ML and Power BI to do sentiment analysis:

Using Azure ML and a free subscription to the Text Analytics API, I’m going to show you how to perform sentiment analysis and key phrase extraction on tweets with the hashtag #Colts (after this past Sunday’s 51-16 beat down of the Colts at the hands of the Jacksonville Jaguars, I’m bathing in the tears of Colts fans. Watch the highlights! ). Although my example here is somewhat humorous, the steps can be used to perform sentiment analysis and key phrase extraction on any text data as long as you can get the data into Power Query.

This is a fantastic example of how Azure ML can be used.  Read the whole thing.

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Power BI Analysis Of Quickbooks Data

Published 2015-12-18 by Kevin Feasel

Rob Collie shows how to use QQube to help with Quickbooks data analysis:

Our financials are the logical first place to start.  And our financials are in the hands of our accounting firm.  Specifically, they are stored in Quickbooks.

This, of course, poses a problem.  Because like ALL accounting and ERP systems, Quickbooks is primarily focused on being a great accounting system.  A system that collects, stores, organizes, and routes data.  Quickbooks is NOT an analytics tool.

And being an analytics (or BI or reporting, whatever you call it) tool is a full-time job.  ANY system whose job it is to collect/organize/route data will NEVER be sufficient for reporting and analysis.  NEVER.  I’m not kidding.  We should never expect different, and that’s not a “knock” on these vendors.  It’s just too many missions for any one company to execute.

This is a nice walkthrough of how you can apply visualization and analytics concepts, especially in a small business scenario.

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Visualizing SQL Saturday Data

Published 2015-12-16 by Kevin Feasel

Tamera Clark analyzes SQL Saturday Nashville data:

Select the funnel from the visualizations (1), select track in the field list (2) and drag track to the values box (3). (Image 5 below) Now we need to customize this visualization.  Select the paint brush to edit. (Image 6 below) I recommend giving each of the tracks a different color. Since Tracks are determined by the organizer the data maybe similar so you might want to use the same colors for more than one data point. You should also update the title Count of Tracks by Track sounds silly. Now we have a lovely display of session distribution by track.

She came up with a nice-looking set of information describing sessions and presenters for SQL Saturday Nashville 2016.  I love seeing this kind of thing and hope it becomes mainstream among SQL Saturday organizers (maybe to the point where some of this is built into the SQL Saturday website).

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