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.

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.

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. ‍

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.

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 ever-evolving landscape of data integration and architecture, organizations grapple with many challenges, from controlling exponentially growing observability data to the complexities driven by hybrid clouds, data migrations, integration of new AI/ML services, and the need for swift time-to-market strategies.