Metrics and logs go together like cookies and milk. Metrics tell you when you have a problem, and logs/events often tell you why that problem happened. But it’s always been harder than it needed to be to get both types of data onto a single screen, especially when the sysadmins using the tools aren’t necessarily daily experts in managing those monitoring platforms.

Out-of-memory (OOM) errors take place when the Linux kernel can’t provide enough memory to run all of its user-space processes, causing at least one process to exit without warning. Without a comprehensive monitoring solution, OOM errors can be tricky to diagnose. In this post, you will learn how to use Datadog to diagnose OOM errors on Linux systems.

Today is an exciting day for LogDNA! I have two wonderful announcements to make. First, we’ve officially announced that LogDNA has closed a $25 million series C round led by Emergence Capital. Second, and most importantly, I’m thrilled to share that Tucker Callaway, LogDNA’s current President and Chief Revenue Officer, is transitioning into a new role as the company’s Chief Executive Officer (CEO).

It’s a metaphor that would have been impossible to decode even a decade ago: a Command and Control environment where essential data flows as quickly and intuitively as a map on Uber or Lyft. It’s a way of imagining efficient access to up-to-the-minute mission-relevant information, so that any sensor can make useful intelligence available to any device or effect, on a single screen, in time to make a difference.

When the coronavirus pandemic hit Asia-Pacific back in January, no one knew what to expect. As the first region to grapple with the questions and uncertainties that the virus presented, leaders had to process the new reality and spring into action at record speed. While navigating the shifting landscape has been a unique journey for all organizations, a few things have proven to be consistent.

In Part 1 of this blog series, we took a look at how we could use Elastic Stack machine learning to train a supervised classification model to detect malicious domains. In this second part, we will see how we can use the model we trained to enrich network data with classifications at ingest time. This will be useful for anyone who wants to detect potential DGA activity in their packetbeat data.