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.

I’d like to share some of the best practices we’ve learned on our journey to battle performance issues with the Jaeger tracing tool. Some may say we are experts in logging. We log for a living, and have our log analytics service (which we based on open source ELK Stack) to prove it. We’ve mastered logging to the level where debugging and troubleshooting our system is a no-brainer.

Elastic is prepping for Elasticsearch 8.0, but in the meantime is rolling out upgrades and features with Elasticsearch 7.7. The new version introduces asynchronous search as well as changes with Elasticsearch clusters, mapping, SQL enhancements, snapshots and machine learning. This post will cover just a few of the highlights of the new release. Besides asynchronous search, ES 7.7 also introduces multi-class classification, reduced heap usage, inference time features, and better password security.

Last week, the first OpenObservability conference took place. This event had amazing content contributions from open source project leaders, users, and influencers. We’ve seen massive growth and adoption in the open source observability space from the inspiring work being done across tracing, logging, and especially metrics. The new data stores and capabilities are growing at breakneck speed. There are more choices— yet more complexity—than ever before.

Current cloud investments have been a boost for flexibility and productivity. The challenge is that there is a single point of failure with these platforms, requiring diversity of providers to reduce dependencies. The future of cloud computing will see effects from the ambitions and expansion of cloud providers themselves, as well as potential competition. This is especially true with Amazon, Google, and to a lesser extent Microsoft.

These days, “SIEM” (Security Information and Event Management) is all over the place. SIEM tools work by collecting data from multiple systems and noticing patterns in the data. This adds immediate value to the business by providing insights, security recommendations, and actionable intelligence. Despite being helpful tools for many companies, SIEM tools do have their drawbacks. This article will describe the four main ones and offer suggestions for how they might be overcome.

The large volumes of logs, metrics, and traces generated by scaling cloud environments can be overwhelming, but they must be collected to identify and respond to production issues or other signals showing business or application issues. To collect, monitor, and analyze this data, many teams choose between open source or proprietary observability solutions.