Runbooks, also known as playbooks, are documents that walk you through a certain task with specific steps. For example, a runbook for spinning up a new server might ask some questions about the purpose of the server and its estimated load, then lead you to the appropriate instructions and settings. Runbooks ease the cognitive load of these common tasks by clearly outlining the process for each.

You’ve pored over the MongoDB documentation, crafted highly polished and well-tuned queries, and confidently deployed your new code to production. Everything ran great at first, but once CPU or RAM usage hit a certain point, your queries suddenly slowed to a crawl. What happened, and how can you prepare for situations like this in the future? This is an unfortunate but common scenario with databases like MongoDB.

Machine Learning (and deep learning) applications are quickly gaining in popularity, but keeping the process agile by continuously improving it is getting more and more complex. There are many reasons for this, but primarily, behaviors are complex and difficult to anticipate, making them resistant to proper testing, harder to explain, and thus not easy to improve.

Alien wavelengths are commonplace today. Here a transponder pair from one optical system vendor connects to, and transmits over, the optical line system (OLS) – constituting fixed/reconfigurable multiplexer and amplification elements primarily – from another vendor. (While it can be technically feasible to pair transponders from different vendors, typically this is not done for commercial and operational reasons.)

Some, maybe. But not most. Look, I have this theory. I also have data. And as with most stories in my life, it all starts with a trip to Las Vegas. In 2017, I attended my first AWS re:Invent in Las Vegas. For a while I heard how re:Invent was either a “cloud” or a “developer” event. I now believe neither to be true. Most people I met at re:Invent were operations folks. They were there to learn about monitoring and managing applications running in AWS.

You have management software that you’ve used for your Linux or Windows servers. Can’t you just deploy a Linux agent and monitor a VMware vSphere/ESX server, or a Windows agent to monitor a Microsoft Hyper-V server? This is a very common question that comes up in any discussion on VMware monitoring and virtualization management. After all, when a VMware ESX server boots, the administrator gets to a Linux login prompt and can login to a Linux operating system.

SIEM (Security Information and Event Management) is a kind of software whose purpose is to provide organizations and corporations with useful information. “About what?” you may wonder. Well, about potential security threats related to your business networks. SIEM does this through data collation and by prioritizing all kinds of dangers or threats. In general, we already answered the question “what is SIEM?”, but how does it do it?

The ability of an actor to remain undiscovered or obfuscating its doings when driving a malicious campaign usually affects the gains of such campaigns. These gains can be measured in different items such as time to allow completion of operations (exfiltration, movement of compromised data), ability to remain operative before take down notices are issued, or ability to obtain gains based on for-profit driven crimeware (DDoS for hire, Crypto mining).

When thinking about serverless applications, one thing that comes to mind immediately is efficiency. Running code that gets the job done as swiftly and efficiently as possible means you spend less money, which means good coding practices suddenly directly impact your bottom line. How does logging play into this, though? Every logging action your application takes is within the scope of that same performance evaluation.

I’m a bit of a data nerd. And like many fortunate people, I’m still working from and generally staying home and have a lot spare time on my hands. So what does that combination result in? Graphs!