Operations | Monitoring | ITSM | DevOps | Cloud

Here’s everything you need to know to get started with Dashbird – the complete solution for End-to-End Infrastructure observability , Real-time Error Tracking, and Well-Architected Insights. When working with AWS, One cannot emphasize enough the architectural best practices for designing workloads. One of those best practices is to design the solution in such a way that the monitoring of infrastructure and troubleshooting of errors and problems is achieved effortlessly.

Like many in 2020, executives at this large oil and gas services company made the decision to have most employees work from home and to restrict all nonessential business travel in order to keep their employees safe. The company rose to this challenge at the very outset of the pandemic. A virtual private network (VPN) is the most common and secure way to provide access to company resources and data when employees are traveling or working remotely.

I’m Tim, a Product Design Manager at LogDNA and a massive coffee and magic enthusiast. My team is responsible for creating a beautiful and easy-to-navigate user interface so that you can easily access, and gain value from, your logs. We’ve been working on making our product more accessible and are about to roll out some subtle changes.

After adding new features in June we worked on fixing some minor bugs and improving the stability of our service. We’re happy to announce that besides those fixes, we were also able to introduce a major update to our heartbeat (background job) monitoring. It was requested by many, so let’s take a look at the details!

Archiving is in and your logs are here to stay! We develop features that streamline the log management processes for our users. Logs are information assets, and we understand that you need to retrieve, re-asses and draw insights from your historic logs. observIQ offers a simple integration with Amazon Web Services (AWS) for extended retention. It takes less than 30 seconds to set up and archive logs directly to an S3 bucket in your AWS account.

When you’re troubleshooting an application on Google Kubernetes Engine (GKE), the more context that you have on the issue, the faster you can resolve it. For example, did the pod exceed it’s memory allocation? Was there a permissions error reserving the storage volume? Did a rogue regex in the app pin the CPU? All of these questions require developers and operators to build a lot of troubleshooting context.