From Cloud Dev Station to Terraform landscaping in Azure

Introduction

A lot of people always keep telling me that they love Azure’s Cloud Shell. Oddly enough, I use it more occasionally and find my self using the WSL (Windows Subsystem for Linux) more. If I analyze it a bit, I recon it’s because I want to easily edit & use files with the Azure CLI (etc). Now, the Azure Cloud Shell has a way to persist files! Therefor I embarked on a small test to see what kind of workflow would work whilst working with Terraform and leveraging the Cloud Shell to apply the configurations.

 

Basic Workflow

So what did I come up with? As you know, I’m running my development workstation in the cloud. In addition, I’ve mounted the CloudDrive onto my workstation and cloned my GitHub repo to that location. Next up, I can author my files locally and afterwards push to my repository. As the local files are synced with the CloudDrive, they’ll immediately pop up in my Cloud Shell too. So I can apply them there…

Sounds great? Let’s take it for spin!

Continue reading “From Cloud Dev Station to Terraform landscaping in Azure”

Putting Azure FrontDoor in front of your webapp

Introduction

Today we’ll be going through the process of putting Azure FrontDoor in front (pun intended!) of a web app.

 

Step One) Adding the custom domain

The first stepĀ  is to add a new custom domain to your Frontend hosts ;

Now if you go through this, you’ll see that AFD expects you to link the custom domain to the azurefd.net domain in order to proceed.

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Using Azure DevOps to deploy your static webpage (SPA) to Azure Storage

Introduction

To, without shame, grab the introduction of the “Static website hosting in Azure Storage” page ;

 

Azure Storage now offers static website hosting, enabling you to deploy cost-effective and scalable modern web applications on Azure. On a static website, webpages contain static content and JavaScript or other client-side code. By contrast, dynamic websites depend on server-side code, and can be hosted using Azure Web Apps.

As deployments shift toward elastic, cost-effective models, the ability to deliver web content without the need for server management is critical. The introduction of static website hosting in Azure Storage makes this possible, enabling rich backend capabilities with serverless architectures leveraging Azure Functions and other PaaS services.

 

Which, to me, sounds great! As one of my projects (VMchooser) is actually a static site (VueJS based Single Page App) that could just as well run on Azure Storage (thus reducing my cost footprint). So today we’re going to test that one out, and afterwards integrate it into our existing CI/CD pipeline (powered by Azure DevOps).

 

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Resiliency Patterns in Azure

Introduction

Resiliency patterns in Azure is a very common / return question. Though over the course of time, I’ve noticed there is a lot of confusing around the architectural patterns involved here. This mostly comes down to the basic illusion that HA (High Availability) and DR (Disaster Recovery) are both met when doing a stretched cluster.

 

Overview of all the patterns

 

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Trying out the Azure Front Door Service

Introduction

With the statement “Scalable and secure entry point for fast delivery of your global applications”, Azure Front Door (Service) saw the lights of dawn! The features it promises to bring to the table?

My first thought was… Does this fill the gap where I’m currently leveraging CloudFlare for my personal deployments? So today’s post will be to see what the process of creating the service looks like in real life!

 

Let’s take it for a spin!

After browsing through the marketplace, we select the “Azure Front Door” and press the button to create one. Here I must say that the configuration flow here is a pleasant surprise. First we enter the “basics”…

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Azure Custom Vision AI : From training to deploying the container export on the Azure Kuberenetes Service (AKS)

Introduction

Todays post will be the backend tour of “Frietjes-of-Niet” (translated from Dutch : “French-Frites-or-Not?”). A big part of the mission of Azure is about democratizing technology so it becomes accessible to organizations in order for them to achieve more. AI (Artificial Intelligence) is a key part of that vision.

 

End-to-End Flow

What will be the flow for today?

  • We’ll train a model to recognize fries
  • Next we’ll be exporting that model to be used as a container
  • Afterwards we’ll build that container
  • To end with deploying (and testing) it onto AKS

Sound cool? Let’s get to it..

Continue reading “Azure Custom Vision AI : From training to deploying the container export on the Azure Kuberenetes Service (AKS)”