In this post, l explain how we used Visual Studio Code’s Development Container feature as a stepping stone in our long-term effort to achieve Collaborative Infrastructure as Code. This one step in the process gave a versioned, repeatable working environment and allowed us time to determine the next steps in the effort to achieve IaC.
It can be rough to ask your development team to estimate work based on abstract story point values, especially when they are new to it or to each other. I know this and have experienced this in full.
So in this blog, I am going to share an exercise with you that will give every member of your team the same frame of reference for estimating the size of their work. I call this exercise Story Point Benchmarking.
I have always been interested in maps and GIS data. Whether I am planning a hike or a backpacking outing for scouts or helping the Ozark Trail Association with their website, trail building, and maintenance or needing directions from one place to another, a good map and mapping tools are a necessity.
ArcGIS has always been the gold standard for anything related to maps and GIS data. So, in this blog, we’ll take a look at some of the mapping options that are available with an ArcGIS Developer account and their Mapping APIs.
In this blog, we utilize the strengths of Docker containers to quickly spin up two separate containers that we can utilize for our software development needs – one running the DeepStack API software and the other running a utility to help us get started with the DeepStack API.
The best part is that once we are comfortable with our setup, we could quickly and easily stop and remove the DeepStackUI utility container to free up resources all while continuing to run the DeepStack API software without interruption.
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!