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.
Welcome to an exciting new FREE class that I am launching today! Over the next year (that’s right year!) I will be releasing one module a week detailing how to work with all of the Power BI visuals available in the Custom Visuals Gallery. You might ask why am I doing this? Well The Microsoft Power BI team and the Power BI Community, through the Custom Visuals Gallery, have expanded the data visualization capabilities of Power BI drastically but unfortunately has provided little and in some cases no direction on how to use these the new features. These Custom Visuals are designed by Microsoft on occasion but more often then not the Power BI Community has put in a lot of hard work to provide these great new features for everyone to use. My thought is if the Power BI Community is willing to design and publish these without asking individuals for payment then I would love to provide training on these features to you for free as well.
This sounds like a nice course. Good on Devin for doing this.
Everyone, should validate if they need to apply KB 3138367. msvcr120.dll should be version 12.0.40649.5 or higher.
This is a nice walkthrough with a lot of screen shots, making it easy to follow.
As of today, the latest release of SQL Server is available as a Virtual Machine on Microsofts Azure Platform.
In a matter of minutes you’ll be able to try out all the new featuresthat was added.
If you can’t provision a local server and have Azure credits, this is another way to use SQL Server 2016.
Today is the day: SQL Server 2016 is available for download! You can download all the versions(enterprise, standard, web, express with advanced services, express, developer) of SQL Server 2016 now if you have a MSDN subscription, and you can also create an Azure VM right now that includes SQL Server pre-installed with one of the versions (enterprise, standard, web, express). Lastly, you can also experience the full features through the free evaluation edition (180 days) or the developer edition(you have to sign in to Visual Studio Dev Essentials, a free developer program, before you can download the developer edition).
Even though it’s a day old now, it’s not too late to grab a copy…
Before 2016, you had to manually opt-in by checking a checkbox during installation.
With SQL Server 2016, there’s no checkbox – you’re opted in by default.
I’m actually a huge fan of app telemetry – sending crash reports and usage data back to the application developers in order to help make the app better. I want developers to know how I use their apps, because I want them to improve the parts of the app that I use the most. Heck, I’d be fine if SSMS turned on the microphone while I worked, and then did sentiment analysis. (They would see a very high number of four-letter words tied to the term “IntelliSense.”)
I’m generally fine with sending telemetry results, but I also think the option to disable this should be easier than a registry setting.
Even though I’m taking out UPDLOCKS, the following race condition pattern can still occur
- Session A takes out an UPDLOCK, sees that key 266 does not exist, releases its UPDLOCK, and prepares to insert
- Session B takes out an UPDLOCK, sees that key 266 does not exist, releases its UPDLOCK, and prepares to insert
- Session A runs its insert
- Session B attempts to run its insert, but fails because of a duplicate key error
We need to hold that lock.
Understanding concurrency is one of the toughest parts of being a database developer, especially because it’s historically been difficult to test it. I like what Kendra’s done here, making the process easy to follow.
1. Work backwards
Jot down when you were first notified of the breach and start to retrace the events that led to you being notified. This may mean investigating logs on databases, firewalls, routers and everything else in between. It’s a massive job to sift through all of the log data trying to find that one event that led you to receive that dreaded call. Fortunately, the data analytics field has been used in recent years to speed up the investigative work by pulling together multiple log files and analyzing it for anomalies.
As a technical guy, the “hire a PR firm” part did not come to mind.
While presenting at SQLDay in Wroclaw, Poland, on the Query Store, I was asked a pretty simple question, which takes precedence, the Query Store or a Plan Guide?
One of my favorite answers to questions is “I don’t know” because it gives me the opportunity to learn. Let’s figure this one out together.
I’ll post the code to recreate this experiment within AdventureWorks at the end of the article. I’m doing this because the code for forcing execution plans using Plan Guides can be pretty doggone long (you may need to generate your own XML from a plan on your own system, fair warning).
The answer is not quite as clear-cut as I would have expected, and I’ll be interested to see what others find.
The batch layer stores all the data with no constraint on the schema. The schema-on-read is built in the batch views in the serving layer. Creating schema-on-read views requires algorithms to parse the data from the batch layer and convert them in a readable way. This allows input data to freely evolve as there is no constraint on their structure. But then, the algorithm that builds the view is responsible to manage the structural change in order to still deliver the same view as expected.
This shows a coupling between the data and the algorithms used for serving the data. Focusing on data quality is therefore not enough and we may ask the question of the algorithm quality. As the system lives and evolves, the algorithms may become more and more complex. These algorithms must not be regarded as black boxes, but a clear understanding of what they are doing is important if we want to have a good data governance. Moreover, during the batch view creation, data quality transformations could be done so as to provide data of better quality to the consumer of the views.
Lambda is an interesting architectural concept, as it tries to solve the age-old “fast or accurate?” problem with “both.” Get your fast estimates streamed through a speed layer, but your accurate, slow calculations handled through the serving layer. Definitely check out this article.