My story with this half-baked product (the Dashboard you are about to see), is that I needed some way of tracking performance on a couple of Analysis Services (SSAS) query servers. There are a lot of good posts and talks about how to collect and store performance counters and SSAS logs out there, and I suggest you look into this, this or that, if you need inspiration.
The current data set is about 200K rows, as I am sampling each server every 5th minute.
Both of these are valuable tools in a Microsoft BI environment.
But what if you can’t fix the source data? I was asked this question the other week, and since I had been asked about it before and not come up with a good answer, I decided to spend some time researching the problem.
What I found was that it was relatively easy to write some M code that gave me the correct results, but very hard to write code that performed acceptably well on a large data set (I was testing on a CSV file containing almost half a million rows). Here’s the code for the function I ended up with:
It’s nice to see that Power Query & Power BI have methods to get around this sort of issue, but it sounds like even those methods are limited in value.
I added a column: RollinAvgSteps = AVERAGEX(FILTER(fitbit_export_20160214, EARLIER(fitbit_export_20160214[Date])>=fitbit_export_20160214[Date]),fitbit_export_20160214[Steps])
…which takes the average of my steps to date. There are a bunch of ways to achieve this, but this is the way that I chose. And you can see that the average line is (happily) improving! Oh, and because I pulled down the extract on the 14th, there’s a dip at the end. My numbers were much healthier by the end of the day, and despite spending way too long NOT walking, I did about 7244 steps that day.
You can see the result at http://bit.ly/RobFitbit
I like the rolling average that Rob added in.
My entry is an analysis of the first 35 games played by Stephen Curry from the Golden State Warriors in NBA. The main feature of the entry is a Shot chart which shows the position from which he attempted his shots and the color denotes whether he made or missed it.
Jason also asks you to vote on his contest entry if you think it’s good.
Yesterday the Power BI team released a new version of Power BI, which have included the most wanted feature ever.
The ability to share your reports outside your organisation, and easily do that. The feature was the most upvoted on the Power BI forum, and it show very clearly that Microsoft and the Power BI team is listening to the end users.
Yeah, that’s a DAX-powered, Power BI dashboard, right here in our website – a website that runs on WordPress, which is Linux for crying out loud. Don’t know what Linux is? No worries, just translate it as “there’s zero Microsoft software behind PowerPivotPro.com, and yet – BAM! Power BI, right here!”
And the dashboard in question is a near-real-time view of the traffic on this very site! Check back in an hour and you will be able to “see” yourself on the map (especially easy if you use one of the “rarer” browsers.)
Check out the technical walkthrough if you’re interested in doing something similar yourself.
However, running DMV queries against a Power BI Desktop model (which of course runs a local version of the same engine that powers Analysis Services Tabular and Power Pivot) and more importantly doing something useful with the information they return, isn’t straightforward. You can run DMV queries from DAX Studio but that will only give you the table of data returned; you need to copy and paste that data out to another tool to be able to analyse this data. Instead it’s possible to use Power BI Desktop’s own functionality for connecting to Analysis Services to connect to its own local data model and run DMV queries.
It looks like there are some limitations to this technique, but for quick and dirty work, it works.
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.
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.
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.