Operations | Monitoring | ITSM | DevOps | Cloud

Every network admin knows that network stability and performance are the lifeblood of modern enterprises. Every second of downtime or suboptimal performance can result in lost productivity, dissatisfied customers, and significant financial repercussions. To navigate this high-stakes digital terrain successfully, T professionals and network admins need a comprehensive understanding of their network infrastructure and the ability to manage it proactively.

In this post, we’re going to take a close look at IIS (Internet Information Services). We’ll look at what it does and how it works. You’ll learn how to enable it on Windows. And after we’ve established a baseline with managing IIS using the GUI, you’ll see how to work with it using the CLI. Let’s get started!

Say hello to the latest arrival in the Azure Integration landscape: Azure API Center, and it’s worth noting that it’s currently in its preview stage!

Keeping your websites and web-based applications running smoothly for your users is essential. External users that can’t get onto your client-facing portals will quickly turn to competitors. Internal users having difficulty with your network will become frustrated which can reduce morale and productivity. Network monitoring via web checks and ping checks could improve website uptime and make everyone’s lives a little easier.

I used to think my job as a developer was done once I trained and deployed the machine learning model. Little did I know that deployment is only the first step! Making sure my tech baby is doing fine in the real world is equally important. Fortunately, this can be done with machine learning monitoring. In this article, we’ll discuss what can go wrong with our machine-learning model after deployment and how to keep it in check.

At Datadog, we have always been deeply involved with open source software—producing it, using it, and contributing to it. Our Agent, tracers, SDKs, and libraries have been open source from the beginning, giving our customers the flexibility to extend our tools for their own needs. The transparency of our open source components also allows them to fully audit the Datadog software that is running on their systems. But our commitment to open source only starts there.

Data growth has significantly out-pacing budgets; the products we use, have to do more. This is where optimization comes into play. Generally, optimization is associated with reduction which may be intimidating…what if something important is reduced? How can you identify what should be reduced? Reduction isn’t about removing context, but about removing repetitive data, meaningless fields, or even flattening JSON.