# Kubernetes Operator Published articles for Kubernetes Operator. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Lyft Moves Streaming Fleet to Apache Flink Kubernetes Operator DevFeed: [Lyft Moves Streaming Fleet to Apache Flink Kubernetes Operator]() Original publisher: [Read original article]() Author: Mark Silvester Published: 2026-09-16T11:00:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [flink](), [apache-flink](), [Kubernetes](), [autoscaling](), [Deployment](), [legacy](), [Testing]() Tags: [apache-flink](), [architecture-design](), [autoscaling](), [deployment](), [development](), [devops](), [flink](), [kubernetes](), [kubernetes-operator](), [legacy](), [lyft-flink-k8s-operator](), [news](), [testing](), [upgrade]() ### AI overview Lyft moved hundreds of production Apache Flink jobs from its in-house Kubernetes operator to the Apache Flink Kubernetes Operator. The change enabled last-state upgrades, in-place autoscaling, and resource autotuning, while Lyft adapted legacy deployment specifications through its deploy API and contributed a fix for a configuration-renaming bug. ### Source excerpt Lyft has moved hundreds of production Flink jobs from a 2020 in-house Kubernetes operator to the Apache Flink Kubernetes Operator, unlocking last-state upgrades, in-place autoscaling and resource autotuning across the fleet. By Mark Silvester ## Safe, versioned Worker Deployments on Kubernetes: Now with autoscaling! DevFeed: [Safe, versioned Worker Deployments on Kubernetes: Now with autoscaling!]() Original publisher: [Read original article]() Author: Brandon Chavis Published: 2026-03-30T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [autoscaling](), [Kubernetes](), [Deployment](), [Open Source]() Tags: [autoscaling](), [deployments](), [kubernetes](), [kubernetes-operator](), [kubernetes-scaling](), [metrics](), [product-news](), [versioning]() ### AI overview Temporal's open-source Worker Controller now supports attaching Kubernetes resources such as Horizontal Pod Autoscalers and PodDisruptionBudgets to versioned worker deployments. Worker versions can be scaled independently using version-specific backlog metrics. ### Source excerpt Automate Temporal Worker autoscaling on Kubernetes. Learn how the new WorkerResourceTemplate attaches HPAs to versioned deployments for independent scaling. ## Blog: Introducing Falco Operator 0.2.0 DevFeed: [Blog: Introducing Falco Operator 0.2.0]() Original publisher: [Read original article]() Published: 2026-03-23T00:00:00Z Content type: release Language: en Sources: [Falco - Falco](), [Falco - The Falco blog]() Topics: [Falco](), [Kubernetes](), [Deployment](), [observability](), [API](), [configuration]() Tags: [api](), [bug-fixes](), [configuration](), [falco](), [falco-operator](), [kubernetes-operator](), [observability](), [release]() ### AI overview Falco Operator 0.2.0 is announced as its first production-ready release. The Kubernetes operator adds a redesigned API, ecosystem component management, artifact and configuration support, enhanced observability, improved reconciliation, and bug fixes. Falco recommends it for deploying and managing Falco on Kubernetes while continuing to support the existing Helm chart. ### Source excerpt Dear Falco Community, today we are excited to announce the release of Falco Operator 0.2.0, the first production-ready release of the Kubernetes operator for Falco! Since the technical preview announced with Falco 0.41.0, we have been working hard to make the operator robust, extensible, and ready for real-world environments. This release brings a redesigned API, a new Component controller for managing the Falco ecosystem, new artifact management capabilities, enhanced observability, and a significantly improved operational model, all grounded in Kubernetes-native patterns. We merged 58 commits since v0.1.1, delivering major new features, 10 bug fixes, and comprehensive architectural improvements. Thank you to all our contributors and the community for your feedback along the way! Going forward, the Falco Operator is the recommended way to deploy and manage Falco on Kubernetes. While the existing Helm chart remains fully supported, we plan to transition to the operator as the standard deployment method. More details on the transition timeline will follow in a future announcement. To learn everything about the changes, read on! What's new? TL;DR Key features: Ecosystem components - deploy Falcosidekick, Falcosidekick UI, and k8s-metacollector as managed components ConfigMap support for rules and configuration, alongside OCI artifacts and inline definitions Structured API types for inline rules and configuration - YAML objects instead of strings Redesigned OCI artifact API with separate image and registry configuration Reference tracking with finalizers to prevent accidental deletion of Secrets and ConfigMaps Enhanced observability with Kubernetes events and status conditions across all controllers Update strategy support for DaemonSet and Deployment modes Server-Side Apply migration for safer, conflict-free reconciliation Key fixes: Plugin initConfig now supports nested configuration objects RBAC compatibility with Kubernetes 1.32+ Spurious update prevention via mana