Operations | Monitoring | ITSM | DevOps | Cloud

Today’s IT environments span legacy infrastructure, multiple cloud platforms, and edge systems—each producing fragmented data, inconsistent signals, and hidden points of failure. This scale brings opportunity, but also operational strain: fragmented visibility, overwhelming alert noise, and slower time to resolution. With good reason, public and private sector organizations alike are moving beyond basic visibility, demanding hybrid observability that’s context-aware and action-oriented.

Open-source package repositories like the Python Package Index (PyPI) play a crucial role in software development. However, these platforms are also potential targets for malicious actors attempting to exploit application software vulnerabilities. The JFrog Security Research team regularly monitors open source software repositories using advanced automated tools, in order to detect malicious packages.

In the fast-paced world of CI/CD, understanding the performance and behaviour of your pipelines is crucial. GitHub Actions has become a popular choice for automating builds and deployments, but anyone who's debugged a flaky workflow or long-running job knows how challenging it can be to get visibility into what's happening under the hood. We usually rely on build logs, timing data, or guesswork when something goes wrong.

This is the first post in a series examining the requirements necessary to achieve operational excellence. In today’s dynamic digital landscape, operational resilience is no longer optional; it’s essential. Organizations must proactively embrace solutions designed to meet tomorrow’s challenges, not just today’s demands. Everbridge xMatters emerges as the clear leader in this space, delivering unmatched automation, sophisticated intelligence, and exceptional adaptability.

Developers lose hours each week to a familiar troubleshooting loop: chase down telemetry across dashboards, decipher vague errors, and juggle alerts to find the signal worth fixing. Production issues, performance regressions, and security vulnerabilities all demand attention, but they often come with little context for taking action.

As your organization grows, you may operate in increasingly complex environments and manage more services and larger teams to maintain them. Evolution like this can lead to an explosion of telemetry data from across your stack, including metrics, traces, logs, and frontend interactions. The benefit of greater visibility is often outweighed by the challenge of acting on the data you collect, and you can easily fall behind on implementing the fixes your services require to operate reliably and efficiently.

As data systems grow more complex and data becomes even more business-critical, teams struggle to detect and resolve issues that impact data quality, reliability, and, ultimately, trust. Engineers have to rely on manual checks and ad hoc SQL queries to catch data quality issues—often after teams relying on the data have noticed something has gone wrong.

VictoriaMetrics Cloud continues to evolve as the most efficient, scalable and open platform in the observability landscape. In our last Q1 update blogpost, we shared new features such as seamless OpenTelemetry integrations, new Organizations support, and improvements in the Explore UI and APIs. This time we wanted to take a minute to showcase how we’re taking the interoperability journey very seriously. Integrations in VictoriaMetrics Cloud Haven’t tried VictoriaMetrics Cloud yet?

Datadog’s Oracle Cloud Infrastructure integration enables you to collect metrics and logs from your entire OCI stack and monitor them within a single platform alongside other third-party technologies. Datadog’s new OCI QuickStart is a fully managed, single-flow setup experience that helps you monitor your OCI infrastructure and applications in just a few clicks.

To efficiently optimize your LLM application before pushing to production, you need a comprehensive testing and evaluation framework. By running experiments, you can optimize prompts, fine-tune temperature and other key parameters, test complex agent architectures, and understand how your application may respond to atypical, complex, or adversarial inputs. However, it can be difficult to manage your experiment runs and aggregate the results for meaningful analysis.