A Fine Slice Of SQL Server
Another important aspect when having datasets is being able to log and monitor performance. In this blog post I am going to compare the logging between Azure Analysis Services (AAS) and Power BI Premium Per User (PPU).
With the recent release of PPU having integration with Log Analytics it makes it a lot easier to compare the logging options between AAS and PPU.
This is an area where there’s still a bit of a gap. Click through to see what the differences look like today.
Leave a CommentKevin Chant shows off the power of Azure Test Plans:
In this post I want to cover using Azure Test Plans for Data Platform deployments. Because using it to manage test plans can be very useful.
By the end of this post, you will know what Azure Test Plans are and how they can be useful for data Platform deployments.
Click through to see how this feature in Azure DevOps works and how you can use it to test your deployments.
Leave a CommentMarc Lelijveld continues a series on going from small-scale to enterprise with Power BI:
Let’s start with the most important feature of the Power BI Service, sharing content! At the same time, this can be one of the most challenging ones. Especially since there are many ways to share content in Power BI. In my experience in enterprise organizations, I have seen a various ways of sharing content. Below I explain the different options there are, leading to a conclusion of my personal best practice.
But why is the way how we share content so important in relation to large enterprise solutions? Well, I believe that all centrally managed solutions should match (organizational) best practices. The way how the content is made available to the users is one of these best practices. It will help end users to find the content they are looking for, always at the same consistent location.
Read on for several techniques.
Leave a CommentI finally got around to trying out a reinforcement learning exercise this weekend in an attempt to learn about the technique. One of the most interesting blog posts I read is Andrej Karpathy’s post on using reinforcement learning to play Pong on the Atari 2600. In it, Andrej uses the Gym package in Python to play the game.
This won’t be a post diving into the details of how reinforcement learning works; Andrej does that far better than I possibly could, so read the post. Instead, the purpose of this post is to provide a minor update to Andrej’s code to switch it from Python 2 to Python 3. In doing this, I went with the most convenient answer over a potentially better solution (e.g., switching
xrange()torange()rather then re-working the code), but it does work. I also bumped up the learning rate a little bit to pick up the pace a bit.
Click through for the (slightly) updated code.
Leave a CommentPatrick Neff shows us a couple of ways to build a Kafka-to-R pipeline:
In Data Science projects, we distinguish between descriptive analytics and statistical models running in production. Overall, these can be seen as one process. You start with analyzing historical data to gain insights, find correlations, and finally develop and optimize your model. Then you transfer it and use it in your running system. A key point for every data scientist is not just the mathematical skills themselves, but also how to get the data into your analytics program.
In this blog post, we focus exactly on this crucial step: retrieving the data. In a second article, we’ll talk about running your model on real-time data.
Click through for the techniques.
Leave a CommentRobert Cain continues a series on fun with Powershell and strings:
Specifically, this can control the output when we embed a numeric value inside a string. Passing in special formatting instructions will make it easy to display values with commas, as currency, or even as hexidecimal.
For all of the examples, we’ll display the code, then under it the result of our code. In this article I’ll be using PowerShell Core, 7.1.3, and VSCode. The examples should work in PowerShell 5.1 in the PowerShell IDE, although they’ve not been tested there.
Robert has quite a few examples, so check them out.
Leave a CommentJoy George Kunjikkur enumerates reasons why you might want to use Kubernetes to host applications:
I started writing this post 2-3 years back. Mainly when Apache Spark 2.3 started supporting Kubernetes (K8s) in 2018. It was obvious that Kubernetes is taking over app hosting space the same way virtual machines took over physical machines. All are expected to understand where the industry is moving and adopt. Hence I paused this post as there is nothing I need to endorse. But it’s time to resume this post and publish it.
Click through for a slew of thoughts on the topic.
Leave a CommentArun Sirpal gives us a brief introduction of Azure Kubernetes Service:
You have the ability to run these on-premises (complex) or in a cloud service, like AWS or Azure. Hence AKS – Azure Kubernetes Service which helps reduce the complexity and operational overhead of managing Kubernetes by offloading much of that responsibility to Microsoft. You may be wondering how does containers relate to this? It was something on my mind when I first entered into this technology. Remember that containers is the next step beyond traditional virtualisation, you can run SQL Server Linux in containers, as an example. I then look at AKS as the “management” layer of the container solution, carrying out tasks such as scheduling, scaling, health, load balancing and host management.
Click through for more information.
Leave a CommentKenneth Fisher puts on a lab coat:
Hypothesis: If I have Dynamic Data Masking enabled on a column then when I use something like BCP to pull the data out it should still be masked.
I’m almost completely certain this will be the case but I had someone tell me they thought it would go differently, and since neither of us had actually tried this out it seemed like time for a simple experiment.
Click through for the experiment and its results.
Leave a CommentSanjay Rathore contrasts two of the three key Apache Spark languages:
Imagine the first day of a new Apache Spark project. The project manager looks at the team and says: which one to choose, scala or python. So let’s start with “scala vs python for spark”.
You may wonder if this is a tricky question. What does the enterprise demand say? Is this like asking iOS or Android? Is there a right or wrong answer?
So we are here to inform and provide clarity. Today we’re looking at two popular programming languages, Scala and Python, and comparing them in the context of Apache Spark and Big Data in general.
Read on for the comparison. I’m at a point where I think it’s wise to know both languages and roll with whichever is there. If you’re in a greenfield Spark implementation, pick the one you (or your team) is more comfortable with. If you’re equally comfortable with the two, pick Scala because it’s a functional programming language and those are neat.
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