Observability is crucial for maintaining complex systems’ health and performance. In its traditional form, observability involves monitoring key metrics, logging events, and tracing requests to ensure that applications and infrastructure run smoothly. The emergence of Artificial Intelligence (AI) promises to revolutionize the way organizations approach observability.

Real user monitoring (RUM) began as a straightforward approach to tracking basic web performance metrics. Focused on things like page load times and response rates, RUM relied on server-side logging and simple browser timings. While these tools captured Core Web Vitals (CWVs), they offered limited insights into how users actually interacted with pages, focused mainly on server-side performance.

Chasing false alerts — or worse, having your system go down with no alerts or telemetry to give you a heads-up — is the nightmare we all want to avoid. If you’ve experienced this, you’re not alone. Before joining Splunk, I spent 14 years as an observability practitioner and leader for several Fortune 500 companies and in my 2.5 years with Splunk I have had the opportunity to work with customers of all shapes and sizes.

The complexity of modern software systems has reached unprecedented levels. Comprehensive monitoring and observability have become paramount as organizations continue embracing cloud-native architectures, microservices, and distributed systems. Enter full stack observability - a game-changing approach that's revolutionizing how we understand and manage our IT environments.

Strong observability in cloud environments is essential for monitoring the health of interconnected systems. Unlike traditional monitoring, which is limited to specific cloud stacks or devices, observability provides comprehensive visibility across the entire hybrid IT infrastructure including applications, IT systems and services.

david.arrowsmith • Oct 03, 2024 In today’s competitive and fast-paced retail environment, service availability is paramount to delivering exceptional customer experiences. As an ITOps Manager or Site Reliability Engineer in a large retail enterprise, you're tasked with managing complex, interdependent systems that support vital business functions such as supply chain operations, point-of-sale (POS) systems, and inventory management.

The massive growth of observability data isn’t limited to just log data. Metrics are growing just as fast, or faster. Making matters worse, DevOps and Engineering teams aren’t just dealing with the increasing volume of metrics data causing a spike in egress, storage, and compute costs. Many tools also charge by the number of custom metrics they track.

Hint: Append them to your structured logs or spans.

Has your organization finally developed that game changing generative AI application? Is your CTO, CIO, or CEO banking on it being a success? I bet they are! Now, here’s the big question: Are you prepared to monitor and troubleshoot your new application once users get engaged? Fear not, my boy Derek Mitchell has you covered with two incredible Splunk Lantern articles which goes deep into how Splunk Observability Cloud allows you to instrument GenAI apps to gain critical observability insights.