Operations | Monitoring | ITSM | DevOps | Cloud

In an era where hybrid environments and AI-driven innovations redefine enterprise operations, organizations face increasing complexity, disruption, and vulnerability in their systems. To overcome this growing challenge, Cisco and Splunk are working together to harness the power of AI to help customers ensure that digital resilience is an inherent part of their systems.

As businesses scale, so do their observability needs, but many find themselves stuck with costly, inflexible platforms that no longer serve them. Despite mounting frustrations, the complexity of migration keeps companies from making a change. The risk of losing critical data, disrupting workflows, or rebuilding everything from scratch often outweighs the benefits of switching. Most vendors offer little to no migration support, forcing teams to manually reconfigure dashboards, alerts, and integrations.

Observability is essential for maintaining and scaling modern applications. With.NET 8, Microsoft has enhanced support for observability using OpenTelemetry. In this post, we explore how to monitor.NET 8 applications logs with SigNoz, an open-source observability platform, using the OpenTelemetry Protocol (OTLP) exporter.

Log volume is exploding, costs are rising, and most teams are stuck duct-taping together short-term fixes. During our webinar, "Optimizing Log Management in Datadog: Cut Costs Without Losing Insights," we discuss how DevOps and engineering leaders are navigating the growing pains of observability, especially in environments where tools like Datadog are mission-critical but challenging to manage. Here’s a recap of the key takeaways.

Migrating to a new logging platform can be a complex operation, especially when it involves both active and historical logs. Observability Pipelines offers dual-shipping capability, making it easy to route active logs to your new platform without disrupting your log management workflows. But migrating years worth of historical logs—which are critical for investigating security incidents and demonstrating compliance with applicable laws—requires a different approach.

Linux log files are text-based records that capture system events, application activities, and user actions. They're stored primarily in the /var/log directory and provide essential information for debugging issues, monitoring system health, and maintaining security. This guide covers the most important Linux log files and a few detailed techniques for reading and analyzing them.

Managing logs at scale is no longer just about storing text—it’s about gaining insights fast, keeping systems healthy, and troubleshooting in real time. With cloud-native architectures becoming the norm, the pressure is on for modern teams to adopt log management tools that are fast, scalable, and easy to use. But with so many options, how do you choose the right one?

In modern observability stacks, log collection is a critical component. Among the most widely adopted logs collector are Fluentd and Logstash. Both tools are designed to collect, process, and forward logs to various destinations like Elasticsearch, Kafka, and cloud services. However, the differences between FluentD and Logstash lie significantly in their design, performance, plugin ecosystems, and user experiences.

Inside an AI engineer’s creative workspace.