Operations | Monitoring | ITSM | DevOps | Cloud

Welcome to March! It’s a new month with a new MSP growth habit to start focusing on. In January, I encouraged you to Never Stop Learning to kick off our list of 12 MSP growth habits. In February, I urged you to go out and Find Love in the form of updating your MSP’s social proof for your website and social media feeds. For March it’s all about stepping out of your comfort zone and exploring new market opportunities to help expand your MSP business.

It’s the year 2009, a nice weekend in late spring and a small group of monitoring enthusiasts comes together to discuss how to move forward with the idea of forking Nagios. The Icinga team in 2009, just to set the mood. Plans were made to make it faster, easier, more scalable, and simply better. Of course, such a project has a lot of hurdles to take – the most important one was of course: the name.

Budgets in IT departments are tight these days, so proving a return on investment is essential for justifying or expanding a project. The good news is that automation saves money by reducing the amount of human effort required. It is similar to investing in a robot vacuum cleaner. Despite the upfront cost, you save time (and money) by not having humans do the vacuuming. Reporting the value delivered by an automation program can be challenging since the value depends heavily on what is being automated.

By kickstarting a monitoring project with Prometheus, you might realize that you get an initial set of out-of-the-box metrics with just Node Exporter and Kube State Metrics. But, this will only get you so far since you will just be performing black box monitoring. How can you go to the next level and observe what’s beyond? They are an essential part of the day-to-day monitoring of cloud-native systems, as they provide an additional dimension to the business and app level.

We’ve all been in the situation before: it’s Friday at 5 PM and the only on-call engineer available to handle incidents is about to hit the slopes. Unfortunately, at that very moment, a customer reports to support that they are unable to access the company’s ecommerce website to complete a purchase. Internal monitoring systems seem quiet and services appear available on internal health dashboards.

As organizations are adopting more of the FinOps foundation practices and trying to optimize their cloud-computing costs, engineering plays an imperative role in that maturity. Traditional troubleshooting of applications nowadays relies heavily on static logs and legacy telemetry that developers added either when first writing their applications, or whenever they run a troubleshooting session where they lack telemetry and need to add more logs in an ad-hoc fashion.

Since the very first version, Epinio has made use of an internal S3 endpoint to store the user’s projects in the form of aggregated tarballs. Those objects are then downloaded and staged by the internal engine’s pipeline and, finally, they are deployed into the Kubernetes cluster as consumable applications. Epinio makes use of S3 as an internal private service. In this scenario, S3 can be thought of as an internal ephemeral cache with the purpose of storing temporary objects.