Redis is a popular open-source, in-memory data store used across industries by a variety of companies, including Github, Stack Overflow, Twitter and Netdata! Redis supports multiple use-cases including.

PostgreSQL is a popular open source object-relational database system designed to work for a wide range of workloads from single machines to data warehouses to web services with many concurrent users. PostgreSQL runs on all major operating systems and is used by teams and organizations across the world, including Netdata. If you are using PostgreSQL in production, it is crucial that you monitor it for potential issues. And the more comprehensive the monitoring the better!

The open-source community is about to benefit greatly from Netdata’s new Grafana data source plugin, which makes use of a powerful data collection engine. This new plugin maximizes the troubleshooting capabilities of Netdata in Grafana, making them more widely available. Some of the key capabilities provided to you with this plugin include the following.

Monitoring and troubleshooting; unfortunately, these terms are still used interchangeably, which can lead to misunderstandings about data collection strategies. In this article we aim to clarify some important definitions, processes, and common data collection strategies for monitoring solutions. We will specify the limitations of the described strategies, as well as key benefits which can potentially be also used for troubleshooting needs.

Following on from the recent launch of our Anomaly Advisor feature, and in keeping with our approach to machine learning, here is a detailed Python notebook outlining exactly how the machine learning powering the Anomaly Advisor actually works under the hood. Or if you’d rather watch a video walkthrough of the notebook then check out below. Try it for yourself, get started by signing in to Netdata and connecting a node.

A month ago, we introduced unsupervised ML & Anomaly Detection in Netdata, the Anomaly Advisor. Today, we’re happy to announce that we’re bringing anomaly rates to every chart in Netdata Cloud. Anomaly information is no longer limited to the Anomalies tab and will be accessible to you from the Overview and Single Node View tabs as well. This will make your troubleshooting journey easier, as you will have the anomaly rates for any metric available with a single click.

As of v1.35.0 the Netdata Agent can now run Metric Correlations (MC) itself. This means that, for nodes with MC enabled, the Metric Correlations feature just got a whole lot faster! The Netdata Metric Correlations feature uses a Two Sample Kolmogorov-Smirnov test to look for which metrics have a significant distributional change around a highlighted window of interest.

Sometimes a hat is just a hat, the truth is just the truth, and the clearly most popular example of a category is plain to see. In this case, Ubuntu is the most popular Linux distribution currently available. With the operating system’s superior popularity also comes an amazing amount of community support.

Netdata’s new Anomaly Advisor feature lets you quickly identify potentially anomalous metrics during a particular timeline of interest. This results in considerably speeding up your troubleshooting workflow and saving valuable time when faced with an outage or issue you are trying to root cause.

Imagine this: You are an engineer at a startup. You are responsible for keeping all the applications running smoothly and safely in production. At first, you have things under control, but soon enough things start getting more complex.