A 55-minute dive into the “Serverless” framework, an open-source tool that aims to simplify building, packaging, and deploying serverless applications across multiple cloud providers and platforms like AWS, GCP, Azure, and Kubernetes.
The steps described in this blog post allow for the adding of a Logic App on Azure to append rows to an Excel file. It took a lot of trial, and mostly error, to figure out how to describe what I wanted in the way that the Logic App would understand. Hopefully, the steps that I outlined in this blog post will save others the time it took me to come up with them.
Recently, while working for a large healthcare client in New York, I ran into an interesting problem that had slim literature on how to solve it.
Our application is primarily on-prem, but it does leverage Azure for a few functions that are easier to solve in a cloud-native environment. We wanted to be able to monitor those functions using the same Elastic Stack that we use to monitor our on-prem application.
I was tasked with building a bridge between where our functionality logs to in Azure (App Insights), and getting that securely back into our Elasticsearch instance that powers our Elastic Stack.
This blog post will detail the solution I landed on. I hope it will be useful to others that need to solve a similar problem!
On my last two projects, I decided to give Azure Data Studio a try to see how it measured up to SSMS. Azure Data Studio gives you a more modern editor experience. It’s comparable to Visual Studio Code with IntelliSense, source control with GIT, and an integrated terminal for Powershell or SQLMD commands.
Azure Data Studio was built with a data platform user in mind, and its easy editing and export options, built-in charting of query results, and customizable dashboards make it an incredibly valuable tool.
In this post, I’ll go over some of the basics of how to use Azure Data Studio.
Infrastructure as Code (or IaC) is the process of using code and versioning in the same way you do your source code to manage your networks, VMS, and Azure resources. IaC generates the same environment every time it is applied, and it’s an important DevOps practice to use alongside continuous delivery.
The release pipeline executes this model to configure target environments. If you need to make any changes, you edit the source, not the target environment. This allows you to create reliable and stable environments on-demand that can be validated, tested, and repeated.
In this blog, we’ll look at how we can use Azure CLI and Azure DevOps Release Pipelines to make this happen. I’ll walk you through all the steps you need to take to get set up.