Logging is an important topic in software development, especially if you need to analyze bugs and other unexpected events in your production environment. Implementing your logging often seems easy. But as you probably experienced yourself, logging is far more complex than it might seem. That’s why you can find lots of articles about it here on the blog.

Ever since humankind developed the ability to write, much of our progress has been made thanks to recording and using data. In ages long past, notes were made on the production and gathering of resources, the exact number of available soldiers and other important personnel, and were compiled and stored by hand. Because of this documentation method, important information was also prone to being misplaced, lost, or even mishandled.

I hosted a webinar where I covered why logging is important, how to choose a logging provider. And then shared our experience of setting up logging on Kubernetes containers, the Kubernetes logging framework and the logging best practices we’ve implemented internally and supported our customers who run Kubernetes in production.

Healthcare organizations that deploy Epic to support the patients that they see every day have a responsibility to ensure that Epic is always online and performing well. Take for example, a hospital that uses Epic for all its patient services to keep the patients healthy and to ensure their safety Epic needs to be online 24×7. Monitoring and troubleshooting both the underlying infrastructure and Epic are critical to ensuring patients get the care they need.

I set out to find a credit mechanism or hard-coded limit in packets per second in AWS EC2. After all my findings set out in this series so far, I had one more test to perform around t2.unlimited. I wanted to see how “unlimited” it is and the difference it makes in packet throughput on capable instance types. This post is about my findings.