Netflix, Amazon, Google, Facebook, and a host of other companies have adopted chaos engineering, which encourages designing systems to proactively ward off potential issues through testing and the anticipation of failure. When it comes to container orchestration tools like Kubernetes, chaos engineering is a vital tactic for enhancing security.
Metrics and logs have been around for a long time, yet we haven’t adopted common standards for them. Sure, there have been attempts on the metric side with OpenMetrics. Similarly, tracing only got a standardization effort with OpenTracing just a few years ago. There was no effort in a unified approach to standardize all observability signals until OpenTelemetry began a little less than two years ago. And there has been a need.
Like everyone else, my life for the last few months has become a never-ending stream of video calls. With Zoom calls, and the occasional Skype, Google Meet, or Microsoft Teams, becoming the norm I’ve noticed that the fans on my Macbook have been kicking in and sounding like a tiny jet trying to take off.
Many enterprises have already adopted Kubernetes or have a Kubernetes migration plan in place, making it clear that the platform is here to stay. While it provides a lot of benefits to its users, to take advantage of them, you need to thoroughly learn Kubernetes and how it works in production. Typically, the most difficult aspects of Kubernetes are learned through experience solving real-world problems.
Managing a complex microservice-based architecture requires defending multiple endpoints. Automating security covers a vast amount of tools and methodologies, so making sure they all communicate is critical. Additionally, tool sprawl in any aspect of DevOps requires putting automation to good use. The Logz.io Cloud SIEM focuses on identifying threats. To optimize its effectiveness, we have negotiated and built out multiple integrations tying complementary tools together.
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.
