In a rapidly evolving realm of IT, organizations are constantly seeking peak performance and dependability, leading them to rely on a reliable observability platform to obtain valuable system insights. Logs vs metrics play a vital role, as any full-stack observability guide would tell you, serving as essential elements for efficient system monitoring and troubleshooting. But what are logs and metrics, exactly?
Logz.io is excited to announce Service Overview, a fast and easy way to unify telemetry data and insights across your infrastructure and applications into a single interface. Our Beta users have reported simplified observability, faster time-to-insights, and observability consolidation.
In this guide, we will compare two of the leading data visualization tools based upon open-source software that are available for use for metrics, traces and log analysis. To allow new users to know exactly which solution may be best suited to their needs, we wanted to explore in more depth a comparison between OpenSearch Dashboards and Kibana across various aspects in our latest guide covering the differences between leading open-source software.
Red Hat is a Linux distribution known for its stability, security, and enterprise-grade features. Whether you’re running Red Hat on bare metal servers or virtual machines, monitoring the performance of your infrastructure is essential. In this article, we’ll explore the top performance monitoring tools for Red Hat servers. We’ll compare their pros, cons, and pricing to help you make an informed decision.
In the face of growing security threats and incidents, businesses must prioritize their ability to detect, investigate, and respond effectively. Timely incident response is crucial for maintaining the security and integrity of systems and data. Among the essential tools in the incident response arsenal, log monitoring stands out as a critical component. By closely analyzing logs, organizations gain valuable insights into system events, user activities, and network traffic.
We’re thrilled to announce new feature updates for Logz.io’s Kubernetes 360 to provide deeper visibility and additional troubleshooting capabilities for your Kubernetes environment.
Many of us remember the public service announcements asking parents if they knew where their kids were at night. They were ominous and a little alarming. Asking yourself the same question about your data can be equally as scary, but Cribl Edge can help.
Elasticsearch® recently released time series data streams for metrics. This not only provides better metrics support in Elastic Observability, but it also helps reduce storage costs. We discussed this in a previous blog. In this blog, we dive into how to enable and use time series data streams by reviewing what a time series metrics document is and the mapping used for enabling time series. In particular, we will showcase this by using Elastic Observability’s Nginx integration.
As someone who admittedly gets bored easily, one of my favorite things about working for a company like Cribl is the huge amount of technologies in our ecosystem I get exposure to. Over time, I also get to observe trends in the market – it’s always so cool to see big upswings in adoption for various platforms and tech. One such trend I’ve observed over the last year is a noticeable uptake and presence in the market of Google Chronicle.
The Elastic APM Java Agent automatically tracks many metrics, including those that are generated through Micrometer or the OpenTelemetry Metrics API. So if your application (or the libraries it includes) already exposes metrics from one of those APIs, installing the Elastic APM Java Agent is the only step required to capture them. You'll be able to visualize and configure thresholds, alerts, and anomaly detection — and anything else you want to use them for!
