Over the past two decades, two trends have completely reshaped the IT landscape: cloud computing and microservices. With cloud computing, organizations of all sizes can provision infrastructure and run applications on a global scale within minutes. And with microservices, these organizations can deploy highly scalable distributed workloads just as quickly.

The movies are filthy with examples of artificial intelligence. Some, like the first Terminator, are evil. Some, like the Star Wars droids, work for the good guys. And so many of them are flatly iconic—Blade Runner (both of them), 2001: A Space Odyssey, War Games, Westworld (the movie and the HBO series), Matrix[i] … the list keeps going and going. We aren’t at the point yet when we can get R2 to talk the Millennium Falcon—or the data center—and find out what’s wrong.

IT professionals are now adapting to a remote environment and learning to manage a distributed, homebound workforce. From a technical standpoint, the process of setting up a remote workforce is well-known. For this blog post let’s dive into one step of the process, which is arguably the most important, user experience monitoring.

Security Information Monitoring or Security Event Monitoring is part of Security Information Management. Yes, I acknowledge they are flashy names and that even experts have their differences about concept and scope. Here in Pandora FMS, flexibility is part of our name, so, hereby, I will abbreviate it as Security Monitoring. As you can see, it is short and manageable!

Here is an overview of the features & fixes we released over the last ~2 months.

Software development and delivery is an ever-changing landscape. Writing software was once an art form all its own, where you could write and deploy machine code with singleness of purpose and no concern for things like connecting to other computers. But as the world and the variety of systems that software supports became more complex, so did the ecosystem supporting software development.

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.

When it comes to business, data is everything. Whether it’s sales, supply, marketing, or your I.T. systems, every day is a constant stream of decisions to be made. To make the right decisions, you need enough knowledge. To have enough knowledge, you need the right data. What’s the best way to collect all this data? In the information age, there’s so much data out there, all the time.

As modern development practices evolve at rapid rates we must stay focused on what makes for a great experience; fast, flawless software. In the pursuit of building fast, performant software, you’ve likely come across performance monitoring products like Real User Monitoring (RUM) and Application Performance Monitoring (APM). In this article, we go deeper into how using RUM and APM can help you and your team build better software experiences.