Operations | Monitoring | ITSM | DevOps | Cloud

Many often wonder, “Is there a difference between observability and monitoring?” The thing is as IT environments have become more complex, monitoring alone has become increasingly less effective. That’s because while monitoring is crucial, it isn’t particularly suited to tracking unforeseen or unexpected turns of events. That’s what observability is meant for. This guide will clarify what observability and monitoring are – and how they differ.

Your business could be spending tens of thousands on SaaS applications—but are you really using them to your full advantage? Research says probably not. Today, the average business uses 130 SaaS apps for day-to-day operations, a number that has steadily climbed over the past decade. Of those, 65% fall under the veil of shadow IT, meaning the IT team is unaware they were ever adopted.

Time series data is becoming more prevalent across many industries. Indeed, it is no longer limited to financial data. As the need to handle time-stamped data increases, the demand for specialized databases to handle this type of data has also grown. The solution: Time series databases. In this introduction guide, we'll explain all the basics you need to know about time series databases, including what they are, how they work and are applied, and some of their benefits.

Service level agreements (SLAs) are essential for defining and managing the performance of services provided by internal or external providers. This article explores the different types of service agreements, their key components, and best practices for creating and enforcing effective agreements.

HTTP requests are at the heart of communication on the World Wide Web. This article will provide an in-depth look at what HTTP requests are, how they work, and their role in enabling the web experiences we rely on every day.

Numerous companies keep launching AI/ML features, specifically “ChatGPT for XYZ” type productization. Given the buzz around Large Language Models (LLMs), consumers and executives alike are growing to assume that building AI/ML-based products and features is easy. LLMs can appear to be magical as users experiment with them.