September is here, and that means many retailers have already begun preparing for the upcoming holiday season. One weekend in particular tends to be the real-life stress test that companies have come to develop a love-hate relationship with: Cyber Weekend. Or more specifically, Black Friday, Cyber Monday, and the weekend in between.
Observability is one of the most critical ways to improve visibility and control over complex software systems. If you've ever wondered how observability differs from monitoring, then this guide will explain some of the key differences between these two popular concepts.
In my previous blog, I discussed how continuous observability can be used to deliver continuous reliability. We also discussed the problem of high change failure rates in most enterprises, and how teams fail to proactively address failure risk before changes go into production. This is because manual assessment of change risk is both labor intensive and time consuming, and often contributes to deployment and release delays.
One of the core features of Cribl Stream is our Replay capability. We pride ourselves on giving customers choice and control over their data. The ability to archive data in cheap object storage, and then providing the ability to reach into the same object storage is one example of this. It’s safe to say that S3 and AWS have become synonymous with the term object storage. It’s like a modern day Kleenex, or Band-Aid.
After months–or potentially, years–of hard work by teams across a gaming enterprise, when the day arrives for a game launch, the last thing your enterprise needs is slowdowns, glitches, outages or poor performance. It’s the death knell for any game, because for your avid gaming customers, there’s always something else (read: a game that isn’t yours) to check out.
An organization at Level 2 in the Observability Maturity Model has built on the foundation of their monitoring capabilities and taken the first steps into observability. In recent years, two major trends have driven the need for the deeper insights that observability can provide.
Artificial Intelligence for IT Operations (or AIOps for short) continues to be a hot topic among developers, SREs, and DevOps professionals. The case for AIOps is especially crucial given the expansive nature of today’s observability efforts across hybrid and multi-cloud environments. As with most observability platforms, it all starts with your telemetry data: metrics, logs, traces, and events.
In the last half-decade, AIOps and observability have arguably been the hottest two topics in IT operations management. Gartner first mentioned AIOps—Artificial Intelligence for IT Operations—in 2016, defining it as using big data and machine learning to automate IT operations processes, such as event correlation, anomaly detection and causality determination.
Distributed denial-of-service (DDoS) attacks have been a continuous threat since the advent of the commercial internet. The struggle between security experts and DDoS protection is an asymmetrical war where $30 attacks can jeopardize millions of dollars for companies in downtime and breaches of contract. They can also be a smokescreen for something worse, such as the infiltration of malware.
In this post, we’ll look at how to host the OpenTelemetry Collector in Azure Container Apps. There are a few gotchas with how it’s deployed, so hopefully this will stop you from hitting the same issues. If you don’t care about the details and just want to run a script, I’ve created one here.
