We at Komodor are excited to announce our groundbreaking integration with Cisco Full-Stack Observability (FSO). This collaboration marks a significant milestone in Kubernetes Continuous Reliability, bringing together the best of both worlds to redefine Kubernetes management.

Unified observability experience from Cisco delivers seamless Observability across your hybrid and cloud native applications. Focus on lower Mean Time to Resolution (MTTR)! In today’s press release, Cisco Unveils New Innovations on the Cisco Observability Platform, we announced a host of exciting innovations.

In today’s economic and regulatory environment, data sovereignty is increasingly top of mind for observability teams. The rules and regulations surrounding telemetry data can often be challenging to interpret, leaving many teams in the dark about what kind of data they can capture, how long it can be stored, and where it has to reside. In the past, addressing these issues at scale was a costly endeavor.

In today’s complex data landscape, continuous profiling has become essential for detailed insights into application resource usage. Grafana Labs is now advancing this field with the introduction of Span Profiles in Grafana 10.3. The Span Profiles feature represents a major shift in profiling methodology, enabling deeper analysis of both tracing and profiling data. Traditional continuous profiling provides a system-wide view over fixed intervals.

Kubernetes has become the world's leading container orchestration platform, aiding small-scale to large-scale businesses in automating, autoscaling, and managing application deployments. Before delving deeper, let's understand why cloud-native solutions like Kubernetes have become the world's—especially organizations'—favorite technology. Creating highly scalable, resilient applications requires flexible infrastructure management.

Observability Pipelines have become vital tools for DevOps and Security teams to manage, control, store, route, and optimize telemetry data analyzed by Security Information and Event Management (SIEM), Application Performance Monitoring (APM), and Log management platforms. These teams spend hours every week trying to fit an increasingly large volume of data into the same size box.

Observability strategies are needed to ensure stable and performant applications, especially when complex distributed environments back them. Large volumes of observability data are collected to support automatic insights into these areas of applications. Logs, metrics, and traces are the three pillars of observability that feed these insights. Security data is often isolated instead of combined with data collected by existing observability tools.

“Mommy, where does software come from?” “Software grows in a circle, just like this!” The software development lifecycle (SDLC) is always drawn as a circle. In many places I’ve worked, there’s no discernable connection between “5. Operate” and “1. Plan.” However, at Honeycomb, there is. More on that later.

Whether you're a system administrator, a developer, or any other DevOps or Site Reliability Engineering (SRE) professional, you know that staying ahead in cloud-native computing is crucial. One way to keep your competitive edge in the technology game is to embrace the benefits of eBPF (Extended Berkeley Packet Filter). On top of advances in security and networking, eBPF-based tools are particularly impacting the observability landscape.

CloudFabrix, the Robotic Data Automation Fabric inventor, announced “Data Observability Pipelines” for dynamic Data Ingestion and automation for any data source and destination. The solution acts as a data management and integration service that uses robotic processes to automate data tasks, such as data integration, data ingestion, cleansing, transformation, and enrichment. Automated data management saves time, improves data quality, and streamlines data workflows.