On May 27, the first OpenObservability Conference was held to bring together leaders, practitioners, and users of leading open source observability tools for sessions on the experiences, strategies, and future of the industry. For the Logz.io team, as long-time proponents of open source, it was rewarding to see everyone come together to explore the challenges and opportunities of open source observability.
Because of its popularity and widespread adoption, Kubernetes has become the industry’s de facto for deploying a containerized app. Google Kubernetes Engine (GKE) is Google Cloud Products’ (GCP) managed Kubernetes service. It provides out-of-the-box features such as auto-scaling nodes, high-availability clusters, and automatic upgrades of masters and nodes. In addition, it offers the most convenient cluster setup workflow and the best overall developer experience.
AIOps is a term Gartner invented to describe a general trend of applying AI techniques to IT Operations data sources to provide additional insights and scale to the teams operating today’s complex software system. AIOps is essentially a feature or set of features to analyze, combine, and collect data. Unfortunately, the lack of AI in these solutions often turns many people off, but this promise is still possible.
Serverless computing has taken off in recent years with more efficient cloud services. AWS Lambda is a great example of this, where provisioning and management of resources happens from the service’s end. You only have to deal with the code. This article will give a brief overview of AWS Lambda in contrast to EC2 instances, then walk through shipping AWS Lambda metrics to Logz.io.
To understand what’s going on in their environment, DevOps teams usually ship some combination of logs, metrics and traces—depending on which signals they’re hoping to monitor. Each data type will expose different information about what is happening in a system. However, not all of that information will be helpful on a day-to-day basis, which can rack up unnecessary data storage costs. That should require users start to filter telemetry data across their observability stacks.
