Last week (7th September 2022) I spotted a post from EUC and Cloud Veteran Marius Sandu alerting his professional and social media networks to an outage of Azure Front Door that in turn meant a significant number of Azure services and resources were unavailable.

A goal of open-source observability is unifying several different signals to provide the observability everyone wants. It’s always interesting to speak to people on this journey, and how they try to provide it through open-source projects, and the challenges they can face. I was thrilled to host Pranay Prateek on the most recent episode of the OpenObservability Talks podcast.

Have you ever tried to find a bug in a multi-layered architecture? Although this might sound like a simple enough task, it can quickly become a nightmare if the system doesn’t have proper monitoring. And the more distributed your system is, the more complex it becomes to analyze the root cause of a problem. That’s precisely why observability is key in distributed systems. Observability can be thought of as the advanced version of application monitoring.

I have been implementing a couple of features lately that allow users to download files. During this process, I have visited various namespaces and possibilities with ASP.NET Core. In an attempt not to forget what I have learned and in the hope that this knowledge can be used by others, here is a blog post about downloading files from ASP.NET Core 😊 This post will use an ASP.NET Core MVC application as an example since that is what I am using.

Companies are investing heavily in the cloud for the operational and financial benefits. But without a robust cloud cost management strategy in place, the complexity of cloud services and billing can to overspending and unnecessary cloud waste. Being able to accurately predict future cloud spend is one way to more optimize cloud spend and inform budgets.

Most classical, batch-oriented machine learning systems follow the paradigm of “fit and apply”. In an earlier blog post, I discussed a few patterns on how to better organize data pipelines and machine learning workflows in Splunk. In this blog, we’ll review how you can organize your machine learning model in a new way: online learning.