Observability systems are designed to keep tabs on key metrics, identify unusual patterns, and alert teams when things go awry. Despite best efforts, however, these systems are not infallible, and sometimes they send out alerts for issues that don’t exist. This is what we call a false positive. These false alarms can wreak havoc on team efficiency, lead to alert fatigue, and obscure genuine problems. Let’s delve into what false positives are and why they matter so much.

Amperity required an observability partner to facilitate their transition into the modern engineering era as their previous tooling struggled to support their growth strategy. When customer data is scattered everywhere, how do you put the pieces together to get an accurate customer 360° view? That’s the power of Amperity’s customer data platform (CDP), and the company has been driving customer data innovation for nearly a decade.

In my February 2024 piece The Cost Crisis in Observability Tooling, I explained why the cost of tools built atop the three pillars of metrics, logs, and traces—observability 1.0 tooling—is not only soaring at a rate many times higher than your traffic increases, but has also become radically disconnected from the value those tools can deliver. Too often, as costs go up, the value you derive from these tools declines.

In today’s increasingly dynamic digital world, the need for end-to-end network visibility has never been more critical. These requirements are especially profound for managed service providers (MSPs) and communications service providers (CSPs). MSPs and CSPs find themselves at the epicenter of digital transformation.

In today's digital world, websites and applications are the lifeblood of your business. But ensuring their performance and uptime in a complex IT landscape, with its mix of technologies and systems, is a constant challenge. Imagine a sale overwhelming your online store, causing the website to slow down and frustrated customers to abandon carts. Downtime like this isn't just lost sales; it damages your reputation and hinders innovation as IT teams scramble to fix issues instead of building new features.

People often get confused between Monitoring and Observability and use them interchangeably in the DevOps field. But they are two very unique concepts. Since we work in this sphere, I thought it was ideal to clear up this confusion and give you the right information on it. With most of the application software now adopting several microservices and going for distributed architecture, the need to have a complete overview of your system cannot be understated.

For nearly a decade now, observability has been one of the hottest topics in IT operations. Borrowed from control theory in engineering, observability in IT operations means measuring a system’s performance by the data it generates, typically metrics, events, logs and traces.

Over the past five years, software and systems have become increasingly complex and challenging for teams to understand. A challenging macroeconomic environment, the rise of generative AI, and further advancements in cloud computing compound the problems faced by many organizations. Simply understanding what’s broken is difficult enough, but trying to do so while balancing the need to constantly innovate and ship makes the problem worse.