Operations | Monitoring | ITSM | DevOps | Cloud

From point-of-sale (POS) terminals to cloud-based applications and mobile devices, reliable connectivity is critical to business operations. Even brief disruptions can negatively impact user experiences, resulting in failed transactions, delayed application responses, or repeated attempts to reconnect. Juniper Mist is an AI-powered networking platform that provides insight into wireless environments, including access point performance and radio frequency health.

Kubernetes environments generate a constant stream of signals across clusters, nodes, pods, and workloads. For teams that have standardized on OpenTelemetry (OTel), maintaining ownership of that data is critical. But in practice, many observability platforms require translation into vendor-specific data formats, leading to fragmented product experiences, blank dashboards, and uncertainty about data integrity.

LLM applications rarely crash. They degrade quietly. Once these applications are shipped to production, subtle quality failures become harder to catch with traditional signals. Tone shifts, hallucinated details, off-topic responses, and incomplete reasoning can emerge while latency and token usage look stable.

Many organizations rely on Oracle Fusion Cloud Applications to run core business workflows across finance, HR, and supply chain operations. Because these SaaS-based applications run on Oracle Cloud Infrastructure (OCI), engineering teams have limited visibility into their performance. Without direct access to the underlying stack, they often lack the signals needed to detect regressions or investigate degraded user experience.

Datadog supports managing Datadog configuration as code through the Datadog Terraform provider. As platform engineering practices evolve, we are focused on making this provider more reliable and trustworthy at enterprise scale.

The 2025 State of Containers and Serverless report found that 64% of organizations use the Kubernetes Horizontal Pod Autoscaler (HPA) to manage Kubernetes workload capacity. But only 20% of those deployments scale on custom metrics. The other four-fifths of organizations rely on resource metrics—CPU and memory utilized by their pods—to trigger autoscaling activity.

Cloud environments generate high volumes of security signals every day. With each one, you have to determine if it’s benign, a clear false positive, or something worth investigating. The challenge is needing to make these calls continuously, often without knowing whether any single event is part of a larger attack. Spending too much time investigating benign activity reduces the ability to detect threats elsewhere, and missing a legitimate threat has clear consequences.

Modern organizations often operate from multiple locations. From retail stores to global enterprises, many companies rely on distributed wired and wireless networks to keep business-critical applications online. Aruba Central provides a centralized, cloud-based platform for managing that infrastructure at scale.

Load balancers are the primary entry points to distributed applications. By strategically directing the flow of incoming web traffic to specific endpoints, load balancers help optimize throughput and ensure the horizontal scalability of applications. In modern systems, load balancers often do more than their name suggests: Beyond basic load distribution, they analyze requests and route traffic based on a wide range of variables, such as client identity.

Karpenter is a fast, flexible Kubernetes autoscaler designed to improve cluster performance and cost efficiency. When the cluster doesn’t have capacity to schedule a pod, Karpenter requests additional compute from the cloud provider, specifying a right-sized instance that matches the preferences you’ve set (for example, instance family).