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.
This is the first post in a new series I’m writing on Azure’s Application Insights (AI) service. The goal of the series is to walk through some of the basics for monitoring your Azure hosted services with Application Insights. We will cover topics like instrumentation, monitoring dashboards, and paging alerts.
In this post we have a look at code instrumentation: What is it? What are SLIs? How do we use the Application Insights client libraries? What are some instrumentation best practices?