Observability vs monitoring has been a common topic in DevOps recently. There has been a lot of debate, and I have learned a lot from them when I started my observability journey. Most literature on observability is associated with a particular product or shares a textbook definition. In this blog post, I want to give you a practical understanding of observability and the differences between observability and monitoring with different scenarios and examples. We will cover the following topics here.

In modern observability, Lucene is the most commonly used language for log analysis. Lucene has earned its place as a query language. Still, as the industry demands change and the challenge of observability grows more difficult, Lucene’s limitations become more obvious.

Grafana Faro and xk6-browser are both new tools within the Grafana Labs open source ecosystem, but the pairing is already showing a lot of potential in terms of frontend monitoring and performance testing. Faro, which was announced last November, includes a highly configurable SDK that instruments web apps to capture observability signals that can then be correlated with backend and infrastructure data.

When thinking about holistic end-to-end observability, it can help to start with what you already have. Many government agencies are already strategically ingesting and storing logs — a key component of observability. More than a year and a half after the release of M-21-31, US government agencies continue to work through the logging maturity models outlined in the memorandum.

IT industry veterans have seen significant changes in the way applications are not only developed but also used. This evolution has been driven by several factors, including the ever-growing need for mobility, vast ecosystems, and the increasing demand for faster and more efficient ways to get work done. Let's take a closer look at how apps have evolved over time.

In data management, numerous roles rely on and regularly use telemetry data. The security engineer is one of these roles. Security engineers are the vigilant sentries, working diligently to identify and address vulnerabilities in the software applications and systems we use and enjoy today. Whether it’s by building an entirely new system or applying current best practices to enhance an existing one, security engineers ensure that your systems and data are always protected.

With one key practice, it’s possible to help your engineers sleep more, reduce friction between engineering and management, and simplify your monitoring to save money. No, really. We’re here to make the case that setting service level objectives (SLOs) is the game changer your team has been looking for.

OpenTelemetry is an open-source observability framework that provides a vendor-neutral and language-agnostic way to collect and analyze telemetry data. This tutorial will show you how to integrate OpenTelemetry with Amazon AWS Fargate, a container orchestration service that allows you to run and scale containerized applications without managing the underlying infrastructure.

With more and more applications moving to the cloud, an increasing amount of telemetry data (logs, metrics, traces) is being collected, which can help improve application performance, operational efficiencies, and business KPIs. However, analyzing this data is extremely tedious and time consuming given the tremendous amounts of data being generated. Traditional methods of alerting and simple pattern matching (visual or simple searching etc) are not sufficient for IT Operations teams and SREs.