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 today’s digital world, every transaction is logged to give businesses endless amounts of functional data, and there is near-universal agreement that data insights will be integral to the success of businesses in the future. There is undoubtedly a need for a more data literate workforce.

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.

Monitoring requires a multi-faceted approach if DevOps teams want end-to-end visibility and deep insight into issues. This is especially true in the case of modern microservices applications, which are essentially collections of distributed services that talk to each other over a service mesh. With monolithic applications, requests can be tracked easily from the client to the server and back, but with modern applications, every request passes through numerous services before completion.