Today, we are excited to launch our first Netdata Cloud Insights feature, Metric Correlations, developed for discovering underlying issues more quickly and identifying the root cause more efficiently. Read on to learn more about our approach to developing this new feature, how it works, and the many benefits you’ll find incorporating this into your team’s troubleshooting workflow.
We recently adopted NodeBB as our software of choice for building the Netdata Community. We have many good reasons for why we wanted to provide our community with a proper home online, but I wanted to cover some of the technical reasons for choosing NodeBB for our platform, and the many parallels between the NodeBB and Netdata projects, which was certainly a driving force behind this decision.
This release broadens our commitment to open standards, interoperability, and extensibility with a new generic Prometheus collector that works seamlessly with any application that makes its metrics available in the Prometheus/OpenMetrics exposition format, including support for Windows 10 via windows_exporter. Netdata will autodetect over 600 Prometheus endpoints and instantly generate charts with all the exposed metrics, meaningfully visualized.
When I first started the project that became the Netdata Agent, I was trying to solve a painful, real-world problem: IT infrastructure monitoring tools fell short when it came to providing complete, granular metrics in real time. Believe me, I had no shortage of tools to choose from, but each of them lacked something either in the ability to see every metric I needed, or see it at the frequency required.
Netdata Cloud works differently from other monitoring solutions. Most solutions limit the number and frequency of metrics because they rely on architectures that aggregate data. Netdata Cloud, however, streams limited metadata from each node running the Netdata Agent, keeping you in control of the data on your systems. The advantage of this architecture is that there is no limit to the number or frequency of metrics, regardless of the scale or complexity of your IT infrastructure.
Sysadmin Day 2020 is right around the corner and we’d like to show our appreciation for all the sysadmins out there who keep IT humming along and come to the rescue to resolve critical issues day in and day out. This year, we’re celebrating all week long by hosting an IT Heroes and Homelabs contest. Join the celebration by retweeting our post with the hashtag #SysadminDay #NetdataWin, and we’ll enter you in a drawing to win some Netdata swag!
In 2019, the Netdata team already knew that a Netdata Cloud solution in the form of an online platform would greatly complement Netdata’s distributed monitoring by making it much easier to organize large infrastructures and by enabling new ways for teams to collaborate. The old node registry available at the time wasn’t enough for Netdata’s users. Building an online platform, even one that does not directly process users’ metrics, is challenging.
IT is advancing blazingly fast. To keep up with architectural changes and hybrid environments, it’s more important than ever to maintain efficient infrastructure monitoring and troubleshooting. Adding to the complexity is the increase of distributed systems, comprised of many components and services.
Deploying and monitoring performance for an entire Kubernetes cluster can be complex. To simplify the process, we’ve added service discovery functionality to eliminate complex configuration, in addition to more advanced monitoring for viewing activity inside containers. Service discovery identifies k8s pods running on a cluster and immediately starts monitoring system performance. All containers are identified, regardless of complexity.
With the rapid growth of security threats to infrastructure, it’s more important than ever to proactively address vulnerabilities. As an open-source project, built on the trust of users and contributors, Netdata has security concerns at its core. Because we’re committed to code security and quality, we apply Agile principles throughout the software development process. A component of this includes regular static analysis.
