I remember when I first started deploying Big Data Clusters, they were on Azure Kubernetes Service utilizing the $200 credit for first time sign ups. By the time I got around to figuring out how to deploy the BDC, not only was my $200 credit gone, but I started to incur cost out of pocket.
If only there was a feature that would allow me to stop the VMs in AKS whenever I wasn’t using them. Well, I’m excited to share that Microsoft AKS (Azure Kubernetes Service) came out with a neat feature (currently in preview at the time of the publishing of this post) that allows you to stop and start your AKS cluster by running a simple command. Of course I had to try it out on BDCs and to my surprise it worked. Well, sort of. Let me explain…
A few months ago I posted a blog on deploying a BDC using the built-in ADS notebook. This blog post will go a bit deeper into deploying a Big Data Cluster on AKS (Azure Kubernetes Service) using Azure Data Studio (version 1.13.0). In addition, I’ll go over the pros and cons and dive deeper into the reasons why I recommend going with AKS for your Big Data Cluster deployments.
This is part 4 of the “BDC series.” You can read part 1 here, part 2 here, and part 3 here. This blog post will go into the available monitoring tools available to monitor the health of your Big Data Cluster. If you’d like to stay updated, without doing the heavy work, feel free to register for my newsletter. I will email out blog posts of my journey down the wonderful road of BDCs.
[Updated for CTP 3.2] – There are kubectl commands and azdata commands to check the health of your cluster but I will focus on the Kubernetes Dashboard for this series. I will blog about some of the useful kubectl and mssqlctl commands in later posts.
This is part 3 of the “BDC series.” You can read part 1 here and part 2 here. This blog post will go into creating the Big Data Cluster on top of the Azure Kubernetes Service (AKS) cluster we created in Part 2. If you’d like to stay updated, without doing the heavy work, feel free to register for my newsletter. I will email out blog posts of my journey down the wonderful road of BDCs.
Before I get started I want to say that there are many ways to deploy a Big Data Cluster. There is a “Default configuration” way and a “Custom configuration” way. You can read more about the custom config way here. I will be posting blogs on the other ways to deploy a BDC but for the sake of this series I will be deploying the BDC via the default way. The BDC team at Microsoft is constantly revamping and tweaking the BDC deployment process in order to make it more streamline and easier.
This is part 2 of the “BDC series.” You can read part 1 here. This blog post will go into creating the Azure Kubernetes Service (AKS) cluster. If you’d like to stay updated, without doing the heavy work, feel free to register for my newsletter. I will email out blog posts of my journey down the wonderful road of BDCs.
If you’d like to stay updated, without doing the heavy work, feel free to register for my newsletter. I will email out blog posts of my journey down the wonderful road of BDCs.
So far, Microsoft does not have a simple way to create a Big Data Cluster. It’s a bit cumbersome of a process and the learning curve is a bit steep. However, Microsoft is currently working on making it easier to deploy a Big Data Cluster via Notebook in Azure Data Studio and eventually some type of “deployment wizard.” But for now, the only option is to do it the long way.
Recently I was in the process of creating a AKS cluster and encountered an error:
Operation failed with status: ‘Bad Request’. Details: Provisioning of resource(s) for container service myfirstcluster in resource group sqlresourcegroup failed. Message: Operation results in exceeding quota limits of standardDSv2Family Cores. Maximum allowed: 20, Current in use: 0, Additional requested: 32. Please read more about quota increase at https://aka.ms/ProdportalCRP/?#create/Microsoft.Support/Parameters/
Installing and deploying a Kubernetes cluster on-prem can be a pain in the arse. Especially if you are new to Kubernetes. That’s where a cloud provider like Microsoft’s Azure comes in handy. Instead of having to go through the arduous task of installing, setting up, configuring and deploying Kubernetes clusters, you can just use Microsoft’s AKS, or Azure Kubernetes Service, to quickly deploy clusters. That way you can focus on your organization’s mission critical issues, rather than worring about ongoing operations and maintenance of your Kubernetes cluster.
Recently, I had to use Azure Data Studio to access a application intent read only secondary replica. I had to use Azure Data Studio because I was using a Mac. I usually use SSMS on my Windows machines. If you want to connect with the “applicationintent=readonly” property via SQL Server Management Studio, you do so by typing it out in the “Additional Connection Parameters” as shown in the screenshot below: