Operations | Monitoring | ITSM | DevOps | Cloud

Last week we introduced Grafana Alloy, our distribution of the OpenTelemetry Collector with built-in Prometheus pipelines and support for metrics, logs, traces, and profiles. We’re excited to see the community embrace Alloy, and we want to help them use and scale it as easily as possible. Many developers that need to deploy and manage software across several hosts turn to Ansible for its ease of use and versatility.

Elastic Universal Profiling™ agent is now open source! The industry’s most advanced fleetwide continuous profiling solution empowers users to identify performance bottlenecks, reduce cloud spend, and minimize their carbon footprint. This post explores the history of the agent, its move to open source, and its future integration with OpenTelemetry.

Relying on their traditional observability 1.0 tool, Pax8 faced hurdles in fostering a culture of ownership and curiosity due to user-based pricing limitations and an impending steep price increase. Pax8’s platform engineering team was keen on modernizing the company’s cloud commerce platform, but they were hitting obstacles with their traditional observability 1.0 tool, which relied on the three pillars of logs, metrics, and traces.

In this livestream, I had a great discussion with Paul Stout and Scott Gray from nth degree about how the service model has evolved from a focus on time and materials to outcome-based services. Watch the full conversation here and leave with a roadmap for improving your next service engagement. Security teams often have a love-hate relationship with onboarding new tools.

If your business is growing, then so too must your IT infrastructure. Servers, VMs, databases, nodes, pods, containers, and all of your digital resources spawn up and down—all in accordance to your business' needs. The catch is all of these infrastructure elements have to be monitored without it being a herculean task to your team to do so. Here are some pain points that arise every time a server or VM is added: Configuration rules will help you solve all these problems and more.

As organizations produce a significant amount of data from varying sources, simple analytics tools can make it challenging and time-consuming to derive insights from this data. Data dashboards can assist with this. A data dashboard is a visual representation of data that offers an at-a-glance view of key performance indicators (KPIs), metrics, and other important information relevant to a particular business, organization, or process.

Kubernetes 1.30 brings a plethora of enhancements, including a blend of 58 new and improved features. From these, several are graduating to stable, including the highly anticipated Container Resource Based Pod Autoscaling, which refines the capabilities of the Horizontal Pod Autoscaler by focusing on individual container metrics. New alpha features are also making their debut, promising to revolutionize how resources are managed and allocated within clusters.

Software debugging has undergone many transcendental shifts. These shifts are as fascinating as the transition from the biological origins of the term ‘debugging’ to its computer science incarnation. The moth that caused the first computer bug has led to a metamorphosis of the debugging scope to cover a much broader role in software development over the years. Live debugging is the latest manifestation of this evolution.

Whether you're a small startup or a large enterprise, the health and performance of your network infrastructure are critical to your success. This is where network monitoring comes into play. Network monitoring involves the continuous observation and analysis of network traffic, devices, and performance metrics to ensure smooth operations, detect anomalies, and troubleshoot issues promptly.

Calculating log analytics ROI is often complicated. For many teams, this technology can be a cost center. Depending on your platform, the cost of a log management solution can quickly add up. For example, many organizations use solutions like the ELK stack because the initial startup costs are low. Yet, over time, costs can creep up for many reasons, including the volume of data collected and ingested per day, required retention periods, and the associated personnel needed to manage the deployment.