Attention: This article was published over 4 years ago, and the information provided may be aged or outdated. While some topics are evergreen, technology moves fast, so please keep that in mind as you read the post.We are excited to announce that Keyhole Software is a Red Hat Ready Partner. This collaboration with Red Hat indicates Keyhole Software’s commitment to …
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!
Microservices has become a catch-all term in the industry used to describe anything from architecture patterns to actual service implementations.
With such a broad spectrum, it can be daunting to know what it is, what it isn’t, and maybe more importantly, why we should care.
In this post, I’ll provide an overview of three kinds of Microservices that I have observed in client architectures: Domain, Integration, and Unit-of-Work. I’ll also discuss specific examples of their purposes and a method to implement each one.
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.
