Operations | Monitoring | ITSM | DevOps | Cloud

In our previous blog post, we outlined why Citrix VAD/DaaS customers using VMware should consider migrating to XenServer. With Citrix now officially supporting XenServer for non-Citrix workloads and Broadcom’s recent licensing changes, many organizations are reevaluating their virtualization strategy.

Modern IT teams rely on dozens, or even hundreds, of third-party SaaS tools. From Workday and Cloudflare to Slack, Zoom, and GitHub, your business depends on these services to function. When a vendor outage occurs, the consequences are immediate: internal tools fail, support tickets spike, and leadership demands answers. Recent events, like the widespread Cloudflare outage in November 2025, highlight how quickly things can spiral out of control.

The Digital Operational Resilience Act (DORA) requires financial institutions to maintain a comprehensive register of their critical ICT third-party providers. This register isn't just a compliance checkbox, it's a strategic tool for managing operational risk and ensuring business continuity. Building an effective DORA register requires systematic identification, classification, and continuous monitoring of your SaaS dependencies.

Last week’s KubeCon Atlanta made one thing abundantly clear, Kubernetes is quickly becoming the de facto platform for AI workloads – with the event lineup chock full of talks, workshops, and even co-located events dedicated to AI, machine learning and running data on Kubernetes natively – with approximately 50 (!) sessions in total focused on AI, ML, LLM, and GenAI topics.. What was until now mostly PoCs and aspirational is now truly delivering in production.

The Tigera team recently returned from KubeCon + CloudNativeCon North America and CalicoCon 2025 in Atlanta, Georgia. It was great, as always, to attend these events, feel the energy of our community, and hold in-depth discussions at the booth and in our dedicated sessions that revealed specific, critical shifts shaping the future of cloud-native platforms.

InfluxDB is the go-to database for developers working with high-velocity time series data for use cases like application performance monitoring and real-time analytics. But InfluxDB exhibits its true power when combined with the right tools and integrations. The tools covered in this blog post can help at all stages of your workflow, from data collection to visualization and analysis, so you can get the most out of your InfluxDB deployment.

For ClickHouse to achieve the speed it's known for, it has historically relied on a trigger-based and incremental architecture for materialized views. This architecture is known to be very efficient but lacks flexibility, such as when working with data that needs to be backfilled rather than append-only.

Elasticsearch has long been the backbone of security analytics for organizations that need fast search, flexible dashboards, and scalable visibility across massive datasets. It powers everything from threat hunting to compliance reporting and real-time investigation. But anyone who has operated Elasticsearch at scale also knows a quiet truth: Elasticsearch is only as strong as the data you feed it. And getting clean, consistent, usable telemetry into Elastic is often the hardest part.

Learn what rogue devices are, why they’re risky, and how continuous discovery plus CMDB workflows shut them down.

Modern AI agents are now highly advanced, frequently becoming essential components of engineering workflows and deployment pipelines. However, operating these systems often feels like trying to navigate a ship through a dense fog. When an agent errors, slows down, or consumes excessive resources, engineers find themselves adrift, lacking the navigational charts needed to diagnose the problem. The absence of deep insight makes debugging, performance tuning, and cost management unnecessarily difficult.