In this post we will do a complete walkthrough for configuring a new continuous integration (CI) pipeline that builds PowerShell modules in Azure DevOps Pipelines. Formalizing your PowerShell build steps into a CI pipeline helps enforce code quality standards and setup a fully automated process for publishing.
I have covered some of these pieces individually in other posts; for example my module starter kit and linting configurations. However a full post is helpful to tie all the pieces together in a detailed guide.
Power BI makes it really easy to import data, create reports with rich visuals, and then gain insights to make decisions. However one of the tricky things that I found when learning Power BI was that most of the example datasets were for sales and marketing data.
When should you use a bar chart? A donut chart? A funnel chart? Existing tutorials answer these questions fine– but what if you have telemetry or metrics for software projects?
In this post I share some Power BI chart and data model examples that are bit more relevant for software engineers. This makes it easier to build the best possible dashboards for your software or production systems telemetry.
This is the third and final post in a series I’m writing on Azure’s Application Insights (AI) service. In the previous post we looked at how to create monitoring dashboards in Azure.
Here in this post we run through some examples for how to configure monitoring alerts with built-in Azure resource metrics and custom instrumented events and metrics.
This is the second post in a series I’m writing on Azure’s Application Insights (AI) service. In the previous post we looked at how to instrument our application code for monitoring.
Here in this post we will walk through how to create application monitoring dashboards directly in Azure using the Azure Dashboards feature and leveraging data from Application Insights and Azure resource metrics.