I wanted to publish a quick note about something a little near and dear to me. As of today, Feb 25th, 2022, Microsoft made the official announcement that they are retiring SQL Server Big Data Clusters. You can read the full statement here.
“Support for SQL Server 2019 Big Data Clusters will end on January 14, 2025.”
I don’t recall how I came across this Kubernetes IDE called Lens, but all I know is it’s cool as hec! It connects to a Kubernetes cluster (using the kube config file) and gives you an in depth view of all the different Kubernetes objects, their associated yaml files, health/metrics, etc. In this blog post I will show you how we can look into a Big Data Cluster’s Kubernetes infrastructure using Lens.
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…
There are a few server-wide configurations that you cannot setup during inital Big Data Cluster deployment. One of those is enabling SQL Agent service. That’s right. If you have deployed a Big Data Cluster you will notice the SQL Server Agent is disabled by default (see screenshot below):
In my previous posts, I showed you how to deploy a single node cluster and a multi-node cluster. That’s find and dandy but how do you upgrade to the newest SQL Server CU? This blog post will show you how to easily upgrade a SQL Server Big Data Cluster. This method applies to a single node or multi-node cluster. It does not matter how many nodes your BDC has, this upgrade process will work.
In my previous post, I talked about deploying a Big Data Cluster on a single node Kubernetes cluster. That’s cool and all but what if you’re a business or organization that cannot have your data on the cloud for whatever reason? Is there a way to deploy a Big Data Cluster on-premise? Absolutely! I’ll walk you through setting that up this blog post. I will walk you through deploying a 3-node Kubernetes cluster, then deploying a Big Data Cluster on top of that.
One of the key components of a Big Data Cluster is the data pool. Within that single data pool, there are two SQL Server instances. The primary job of the data pool is to provide data persistence and caching for the Big Data Cluster. (At the time of this blog post, there can only be a single data pool in a Big Data Cluster and the maximum supported number instances in a data pool is eight.) The instances inside the data pool do not communicate with each other and are accessed via the SQL Server Master instance. The data pool instances are also where data marts are created.
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.
One of the biggest questions I had when I first started diving into Big Data Clusters was, “What about licensing….how will that work?” With so many different instances running on the storage pool, data pool and compute pool nodes will licensing cost too much? The answer I got from Microsoft was that it will “be competitive”.
Before you deploy big data clusters, you must configure the tools below on a Windows or Linux machine that will act like a “base machine” from which you will be able to deploy, manage, and monitor a SQL Server Big Data Cluster. For the example in this blog I will use a virtual machine running Windows Server 2016 running 4 cores and 8 GB RAM. (This can also work on a Windows 10 Pro machine as well).
I love to share real-life stories when I give talks. I usually start out my sessions with a story on how I got interested in Big Data Clusters. It all starts with my neighbor Tom (not his real name) last year (2018). I was at the bus stop with my 5 yr old son on his first day of Kindergarten. As I am waiting patiently for the bus to arrive I hear a voice say,