Operations | Monitoring | ITSM | DevOps | Cloud

Modern IT estates are increasingly complex, generating vast amounts of data – some critical and actionable, but much of it mere noise. Extracting meaningful insights to ensure optimal system health and IT performance is beyond the scope of humans. This is where observability, enhanced by AI and automation, becomes essential.

For teams in many organizations, the work of IT and network management keeps getting more difficult. A recent EMA survey offers some findings that clearly illustrate this point. When respondents were asked which networking skills are the most difficult to find, several roles received a response of 30% or more, including network security, network monitoring and troubleshooting, and data center networking.

Datadog and New Relic didn’t become some of the best observability platforms today by accident. Unlike traditional monitoring tools, both are built from the ground up to be cloud-native. This design is crucial for tracking system health across hybrid cloud infrastructure, modern applications, and microservices/containerized architectures. Both platforms also offer more flexible pricing models than the traditional subscription-based pricing you’ll see elsewhere.

With more and more people adopting OpenTelemetry and specifically using the tracing signal, I’ve seen an uptick in people wanting to add the entire request and response body as an attribute. This isn’t ideal, as it wasn’t when people were logging the body as text logs. In this blog post, I’ll explain why this is a bad idea, what are the pitfalls, and more importantly, what you should do instead.

“Impossibly expensive.”“Generic database metrics.”“Exceeding limits.”“No transparency.” These are the words our customers use to explain why they looked for a Datadog alternative and migrated onto Grafana Labs’ observability solutions. Grafana Cloud provided the scalability that LexisNexis Risk Solutions needed to migrate acquired companies into a unified observability platform. “We’ve had migrations from Datadog.

Have you ever wondered how to navigate the complexities of managing Kubernetes clusters effectively? Observability is the key, and Elasticsearch plays a pivotal role in storing and analyzing the critical data that keeps your systems running smoothly.

As businesses scale across hybrid and multi-cloud environments and integrate AI-powered technologies, complexity grows — and with it, the risk of performance degradation and cost of downtime. To avoid facing customer-impacting IT issues, organizations need better ways to correlate data across environments, detect anomalies before they escalate, and resolve incidents more efficiently. That’s where Splunk and Cisco come in.

There are several big barriers to an effective tracing strategy. Modern applications require complex code instrumentation, and legacy applications might not be so easy to alter, and that’s assuming every engineering team can be engaged to make the necessary changes. eBPF & OpenTelemetry flip this entire problem on its head, and Coralogix is one of the first major observability platforms to leverage this exciting functionality, to provide an unobtrusive, low risk overview of your system.

Observability in Azure isn’t just about tracking metrics—it’s about truly understanding how your cloud infrastructure, applications, and services are performing. It helps you spot issues before they become problems, optimize performance, and ensure security. In this guide, we’ll break down Azure Observability in a way that’s easy to follow, covering key concepts, best practices, and some useful tricks to give you an edge.

88% of online consumers are less likely to return to a site after a bad user experience. This means that addressing frontend issues such as slow load times, broken features, and unresponsive elements is crucial. Frontend monitoring helps development and IT teams proactively catch and resolve these issues to improve their user experience.