When it comes to managing AWS cloud security, a growing concern for security operations (SecOps teams) is the increasing sophistication of digital threats. While conventional cyber threats deploy widely known tools and techniques in crude, all-or-nothing attempts to breach enterprise security controls, sophisticated attacks known as Advanced Persistent Threats (APTs) employ more advanced technologies and methods to gain and maintain access to secure systems for long periods of time.

So, there I was, a newbie to.NET 9.0, Blazor, and Coralogix, standing on the precipice of observability in a world of production bugs and development mysteries. As an Agile enthusiast, I’m well versed in all things “observability” and how it’s a game-changer for root cause analysis, especially in today’s rapid, iterative development cycles. Observability is like getting X-ray vision into your application to understand what’s truly happening based on system outputs.

A look at the expanding roles of SREs and the new skills needed: cost management and AI Imagine the CTO walks into your team meeting and drops a bombshell: "We need to cut our cloud costs by 30% this quarter." As the lead SRE, this might cause a strong reaction — isn’t your job about ensuring reliability? When did you become responsible for the company's cloud bill? If you've had a similar experience, you're not alone. The role of site reliability engineers (SREs) is evolving fast.

Complexity rules the day within the world of data systems and pipelines. A goal for any observability practice is to help reduce complexity and give users and administrators a clear view of what’s happening in any system. This is the path to unified observability, a mature system where monitoring and troubleshooting are streamlined. This has been difficult to achieve for many organizations.

Learn how to configure and manage Linux Syslog for better system monitoring, troubleshooting, and log management with these helpful tips.

Python's logging system provides powerful tools for application monitoring, debugging, and maintenance. This comprehensive guide covers everything from basic setup to advanced implementation strategies, helping you build robust logging solutions for your Python applications.

Large language models (LLMs) are AI models with billions of parameters, trained on vast amounts of data. These models are typically flexible and generalized. The volume and distribution of training data determines what kind of knowledge a large language model can demonstrate. By training these large models on a variety of information from all knowledge domains, these models can perform sufficiently well on all tasks.

Let me set the record straight before anyone accuses me of bias or not being an OpenTelemetry supporter. Cribl loves OpenTelemetry! We’ve written lots of blogs about It; we have vendor-specific OpenTelemetry Destinations (with more to come!), and we support automatic batch parsing for easier data manipulation and re-batching for network transport efficiency of logs, metrics, and traces.

Last week, I attended one of the last conferences of the year with team Mezmo: the Gartner IT Infrastructure, Operations & Cloud Strategies Conference in Las Vegas. Not surprisingly, there were over 20 sessions covering observability and how it is getting increasingly critical in the new complex distributed computing environment. Of course, there were many sessions, including all keynotes that addressed the advent and impact of AI on IT operations and observability.

That’s a wrap on Raygun’s 12 Days of Christmas 2024! Over the past two weeks, we’ve rolled out daily updates featuring bug fixes and feature improvements inspired by your feedback. These small but mighty changes are all about making Raygun faster, smoother, and easier to use. Thanks for helping us level up—your input makes all the difference. Our special thanks to Blair from New Zealand who suggested this great idea!