One of the key performance indicators of any system, application, product, or process is how certain parameters or data points perform over time. What if you want to monitor hits on an API endpoint or database latency in seconds? A single data point captured in the present moment won’t tell you much by itself. However, tracking that same trend over time will tell you much more, including the impact of change on a particular metric.

In case you haven’t seen or used it, there is a “Bulk Actions link” just under the “Add Monitor button” in the left sidebar. It simply opens the “Bulk Actions dialog” and presents a set of actions that can be applied to monitors in bulk. This feature is now more powerful with a few important additions.

We all read them, and we all have our favorite technology news and media sites. Now that mobile dominates the market and Google has shifted to ranking based on mobile performance, we thought it would be fun to see how ready the top 50 technology news and media websites are for the shift using our free Website Speed Test Tool.

Organizations are drowning in alerts, incidents, and chaos that prevent them from doing their jobs and serving their customers. Notably, for businesses who operate always-on services, an outage or downtime can be devastating to their bottom line, not to mention a poor experience for their customers and users.

Node.js is an asynchronous JavaScript runtime that is used to develop highly scalable network applications. To help provide more visibility into these dynamic environments, we’re pleased to announce that Datadog APM has officially released support for monitoring Node.js applications, which joins our existing support for Java, Ruby, Python and Go.

Icinga 2.9.0 provided many many bugfixes and one change has unfortunately been overseen. If you use an init system different to Systemd (e.g. Sysvinit) or the “-d/–daemonize” option in your containers, issue #6445 causes troubles. This is visible with logging, cluster connects, IDO database writing. Users reported problems on CentOS 6, SLES 11, Ubuntu 14, Debian 8, Gentoo (both with Sysvinit).

When we released derived columns last year, we already knew they were a powerful way to manipulate and explore data in Honeycomb, but we didn’t realize just how many different ways folks could use them. We use them all the time to improve our perspective when looking at data as we use Honeycomb internally, so we decided to share. So, in this series, Honeycombers share their favorite derived column use cases and explain how to achieve them.

Managed IT services have undergone significant change and evolution in a very short time. As an IT service provider, remote monitoring and management (RMM) is at the core of your business, allowing you to manage a large number and variety of endpoints. At the same time, your clients have grown to expect 24×7 uptime with outstanding customer service, potentially leading to some growing pains on your end.

Outages and postmortems are a fact of life for any software engineer responsible for managing a complex system. And it can be safely said that those two words – “outage” and “postmortem,” do not carry any positive connotations in the remotest sense of the word.

The fourth industrial revolution, Industry 4.0, is happening today. Recent progress in manufacturing automation requires fewer individuals on a shop floor to do manual work with and at machines. However, those people are now required to respond faster and more effectively to keep manufacturing up and running. Fortunately, the industrial Internet of Things (IoT) allows for applying real-time monitoring, workflow automation, predictive analytics, and other means to new verticals and businesses.