Traditional monitoring methods become inefficient as organizations shift from legacy software systems to complex cloud-native architectures. This transition renders these methods less effective, as they no longer provide the critical insights needed. In response, observability engineering has emerged as an important discipline, offering a more comprehensive understanding of modern software systems. This article will take you through the definition, importance, and processes of observability engineering.

Around 70% of companies experienced cyberattacks in the past year. With this increase in cyberattacks, the importance of log management in IT security has also increased over the years. That’s the reason why small and enterprise businesses have started to invest in log management tools to protect their businesses from cybersecurity breaches.

In the world of data management, Cribl offers various methods to enhance data using the Lookup Function and many C.Lookup Expressions. While Cribl’s documentation is comprehensive, practical examples are often the most effective learning tools. That’s why we’ve introduced the new Lookup Examples Pack.

Last week, I attended the Amazon Web Services (AWS) re:Invent conference in Las Vegas, NV, with 50,000+ others. It was quite a busy week with several keynotes, announcements, and many sessions. While the hot topic at re:Invent was generative AI, I’ll focus my blog post on a few customer sessions I attended around observability: Stripe, Capital One, and McDonald’s. ‍

Years before founding Logz.io, I was a software engineer, working with various tools to ensure my products and services performed correctly. There were few tools I dreaded using more than application performance management (APM), and I know that I’m not alone. I hated traditional APM. It’s heavy. It’s hard to implement. It’s expensive. It takes a very long time to derive business value.

As applications in the cloud become more distributed and complex, the Mean Time To Resolution (MTTR) for production issues is getting longer. Modern systems are built with hundreds of distinct, ephemeral, and interconnected cloud components, which can make it exceptionally hard for engineers to understand the current state of their applications, what problems are impacting customers, and why those problems are occurring.

For extremely high throughput web applications, it is important to load balance the traffic across multiple servers. However, load balancing the traffic alone is not enough at times. The reverse proxy server that handles the workload needs to be performant, too. In our previous article, we discussed the NGINX reverse proxy server and understood how to monitor it. In this article, we set up monitoring for an even more performant reverse proxy server - HAProxy.

Engineering and ITOps teams have an important mission: keeping their software and digital systems performing and reliable. But as we’re about to embrace a new year full of changes, industry shifts, and AI developments, this mission is challenged by increasingly complex environments, technology alternatives, and an overwhelming number of tools available. The result? Overages, tool sprawl, and toil, which all lead to longer times to detect and resolve issues.

The manufacturing industry is at a crossroads. With automation and emerging technologies like AI, organizations are eager to make operational and production processes more efficient. However, for many manufacturers, the rapid pace of digitizing legacy infrastructure and systems has also exposed many unanticipated hurdles, with one of the biggest being the convergence between IT and operational technology (OT).

Cribl Stream is a real-time security and observability data processing pipeline that can be used to collect, transform, enrich, reduce, redact, and route data from a variety of sources to a variety of destinations. One of the popular destinations for Cribl users is Elastic SIEM. This blog post will walk you through the steps on how to set up Cribl Stream to normalize and forward data to use with Elastic Security for SIEM.