Operations | Monitoring | ITSM | DevOps | Cloud

Cloud logging continues to grow in popularity and usage as more organizations transition to storing data in the cloud rather than on-premise storage. This is fueled, in part, due to the numerous advantages that can be gained from cloud logging. For example, cloud logging solutions can scale to increasing data volumes with ease as an organization grows.

I had a fantastic opportunity to sit with Ben Good of Google and Rich Prillinger of Mezmo and participate in the discussion about the new DORA 2024 report. The 10th edition of the DORA report covers the impact of AI on software development, explores platform engineering’s promises and challenges, and emphasizes developer experience and stable priorities for success.

We are excited to collaborate with Dell on the white paper,Agentic RAG on Dell AI Factory with NVIDIA. The white paper is a design reference document for developers outlining strategies and solution components to implement agentic retrieval augmented generation (RAG) applications. It’s a design point for organizations across industries, specifically healthcare, for the agentic RAG framework decision-making with AI-driven data retrieval.

The original premise of observability was to ensure system health, identify issues, and resolve those issues efficiently. As I recently outlined, the legacy approach (sometimes called Observability 1.0 now) relied heavily on metrics and tracing because logs were seen as too noisy or challenging. But, as most forward thinkers have identified now, logs are exactly the telemetry type that we need the most.

We live in an “always on” world, so unplanned outages are more than just inconvenient. They can result in lost revenue, damaged reputations, and, more importantly, frustrated customers. While preventing outages is impossible, the most resilient teams must be prepared with a solid plan, a “technical go bag,” so to speak: a collection of tools, plans, and resources ready to activate at the first sign of trouble.

As Gartner predicts, AI will support up to 70% of performance monitoring and troubleshooting tasks in the next few years. The Logz.io AI Agent helps teams get ahead of this curve today. Too much time spent troubleshooting? You’re not alone. Manual investigation, jumping between dashboards, and piecing together scattered data are time-consuming and frustrating.

The Grafana Loki 3.3 release is here, and it brings a fresh wave of enhancements aimed at making your log management experience faster, more efficient, and more scalable. While this update includes the usual round of bug fixes and operational improvements, the standout feature is a shift in how Loki leverages Bloom filters—going from free-text search to harnessing the power of structured metadata.

Lessons from the front lines: Challenges in your observability maturity journey In our previous blog, we explored the observability maturity spectrum — revealing that while only 7% of organizations consider themselves experts, the majority (43%) are actively working to improve their practices. We saw how mature organizations achieve better outcomes, from faster root cause analysis to reduced user-reported incidents.