In this module you will learn how to use the Hexbin Scatterplot Power BI Custom Visual. The Hexbin Scatterplot is a variation of the traditional Scatter Chart but instead of using bubble size it relies on color saturation and hexbins to show value distribution. You should consider using this chart when you’re more interested in visualizing density instead of individuals points themselves.
This is worth checking out.
To sum up, the workflow for tuning your query is:
Make some changes to the LongQuery query that hopefully make it faster
Update the Trace Message parameter with some notes about which version of the LongQuery query it is that you’ll be testing
Click the Refresh Preview button for the Diagnostics query to test how long LongQuery now runs for
Refresh, or load, the query that reads the data from the trace logs so you can see how all of your changes have affected query execution times
I give it two months before the Power BI team releases a change to make this easier…
When you share a content with an individual in the organization, if that person leave the company, or be replaced by someone else from another team, then you have to remove sharing from previous user account, and assign it to the new user account. Best practice is to share content with groups. and members of Groups then easily can be managed by an administrator. Power BI groups are fully synchronized with Office 365 groups. once you used a group in Power BI, then it is only an admin’s task to add/remove members from it.
I like this group-based approach a lot, as it makes dashboard security a lot easier.
Kibana is the natural UI choice for partnering Elasticsearch, and it has the advantage of being Web-based and Dockerized, so it’s cross-platform and easy to share. But PowerBI is a lot more powerful, and the multitude of available connectors mean it’s easy to build a single dashboard which pulls data from multiple sources.
Using Elasticsearch for one of those sources is simple, although it will need some custom work to query your indexes and navigate the documents to get the field you want. You can even publish your reports to PowerBI in the cloud and limit access using Azure Active Directory – which gives you a nice, integrated security story.
I tend to be very hard on Kibana, particularly because it makes the easy stuff easy and the hard stuff impossible, so I think that this is an interesting alternative to Kibana.
If you already have R installed on the same system as PowerBI, you just need to paste the R scripts in the code pen. Otherwise you need to install R in the system where you are using the PowerBI desktop like this:
This step-by-step guide features a lot of images and should be pretty easy for a new user.
In previous videos you’ve learned that we can demonstrate R visualization in Power BI, In this video you will learn how R visualization is working interactively with other elements in Power BI report. In fact Power BI works with R charts as a regular visualization and highlighting and selecting items in other elements of report will effect on that. Here is a quick video about this functionality
Check out the five-minute video.
In this module you will learn how to use the Enlighten Aquarium Power BI Custom Visual. While it might not be the most practical visualization it does provide a fun way to show categorical data and can have multiple series shown as well.
From now on, all dashboards must look like screensavers from the 1990s.
No data source is needed – this is a way of defining a table value in pure M code. The first parameter of the function takes a list of column names as text values; the second parameter is a list of lists, where each list in the list contains the values on each row in the table.
In the last example the columns in the table were of the data type Any (the ABC123 icon in each column header tells you this), which means that they can contain values of any data type including numbers, text, dates or even other tables. Here’s an example of this
This is a helpful trick.
In the documentation of this service in Meetup mentioned that Time column is:
time = UTC start time of the event, in milliseconds since the epoch
That means it is timestamp formatted. Timestamp value is number of seconds from epoch which is 1970-01-01 00:00:00. I have previously written about how to change timestamp value to date time and it is fairly easy with adding seconds to it. However for this case our value is not seconds, it is milliseconds so I have to first divide it by 1000.
This is pretty cool. We’re starting up a Power BI meetup in the Triangle area, so it’ll be fun hosting a Power BI Meetup where we use Power BI to read Meetup data.
Partnering with Stetson University, I am happy to share the first of many Power BI Higher Education Analytics solutions. This solution shows student persistence, retention, and graduation patterns, leveraging BANNER as the data source. Year-over-over retention and graduation rates can be filtered to allow deeper examination of trends at the college and major level. Additional views, including retention and graduation rate tables by major and ethnicity, are included within the report solution. The entire solution with documentation can be downloaded here.
The following image shows the first view within the report: overall persistence, retention, and graduation rates by year of first time student cohort. This report allows users to quickly show institutional retention and graduation trends across time, with the option to filter the view to show only specific colleges and/or majors.
This also serves as a Power BI demo, in case you’re hurting for good examples.