We all use computers — some of us are on our laptops all day, every day. But how do our personal or work computers connect to the internet, share files, send emails, and complete other tasks? That’s the role of servers.

Observability is a key pillar for today’s cloud-native companies. Cloud elasticity and the emergence of microservices architectures allow cloud native companies to build massively scalable architectures but also exponentially increase the complexity of IT systems.

For this week’s installment of “The concise guide to Loki,” I’d like to focus on an interesting topic in Grafana Loki’s history: ingesting out-of-order logs. Those who’ve been with the project a while may remember a time when Loki would reject any logs that were older than a log line it had already received. It was certainly a nice simplification to Loki’s internals, but it was also a big inconvenience for a lot of real world use cases.

In the business world, shadow IT is a controversial topic. Gartner defines Shadow IT as any IT devices, software and services that are used outside or beyond the ownership or control of IT departments/ organizations. This includes: In a standard work environment, the IT department would be responsible for providing whatever IT solutions and work tools were needed across all business functions.

When exploring data, comparing individual data points with overall statistics for a large data set is often useful. For example, you might be interested in understanding when a performance metric rises above the historical average. Or possibly knowing when the variance of that metric increases past a certain threshold. Or maybe noting a change in the distinct number of IP addresses connecting to your public web portal.

In part 1, we looked at how the design plan of traditional monitoring technologies depended heavily on properties of the systems that were intended to monitor and then showed how those properties began to be undermined by an increase in complexity, an increase which can ultimately be captured by the concept of entropy. In this part, we will explore how increased entropy forces us to rethink what is required for monitoring.

An Observability Pipeline is a transformative tool for managing complex system data, optimizing security, and enhancing performance within budget. Observo.ai revolutionizes this with AI, slashing costs and streamlining data analysis to empower Security and DevOps teams.

This blog post discusses utilizing Cribl Search to pull and visualize data from the AWS API without ingesting data. This will allow you to collect, analyze, and visualize data from your AWS account in real time without ingesting the data first.

The RED method is a streamlined approach for monitoring microservices and other request-driven applications, focusing on three critical metrics: Rate, Errors, and Duration. Originating from the principles established by Google's "Four Golden Signals," the RED monitoring framework offers a pragmatic and user-centric perspective on service performance.

Welcome to Observability 101, the comprehensive guide from Observo.ai, unveiling the mysteries of observability in 38 insightful chapters. Have you ever wanted to know something specific about observability but were afraid to ask? Fear not, this series is designed to be your go-to resource, answering all of the questions you might have.