Graphs in your head, or how to assess a Kubernetes workload, with Oleksii Kolodiazhnyi
Understanding what's actually happening inside a complex Kubernetes system is one of the biggest challenges architects face.Oleksii Kolodiazhnyi, Senior Architect at Mirantis, shares his structured approach to Kubernetes workload assessment. He breaks down how to move from high-level business understanding to detailed technical analysis, using visualization tools and systematic documentation.You will learn:A top-down assessment methodology that starts with business cases and use cases before diving into technical detailsPractical visualization techniques using tools like KubeView, K9s, and Helm dashboard to quickly understand resource interactionsSystematic resource discovery approaches for different scenarios, from well-documented Helm-based deployments to legacy applications with hard-coded configurations buried in containersDocumentation strategies for creating consumable artifacts that serve different audiences, from business stakeholders to new team members joining the projectSponsorThis episode is sponsored by StormForge by CloudBolt — automatically rightsize your Kubernetes workloads with ML-powered optimizationMore infoFind all the links and info for this episode here: https://ku.bz/zDThxGQsPInterested in sponsoring an episode? Learn more.
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Our Journey to GitOps: Migrating to ArgoCD with Zero Downtime, with Andrew Jeffree
Andrew Jeffree from SafetyCulture walks through their complete migration of 250+ microservices from a fragile Helm-based setup to GitOps with ArgoCD, all without any downtime. He explains how they replaced YAML configurations with a domain-specific language built in CUE, creating a better developer experience while adding stronger validation and reducing operational pain points.You will learn:Zero-downtime migration techniques using temporary deployments with prune-last sync options to ensure healthy services before removing legacy onesHow CUE lang improves on YAML by providing schema validation, early error detection, and a cleaner interface for developersHuman-centric platform engineering approaches that prioritize developer experience and reduce on-call burden through empathy-driven design decisionsSponsorThis episode is brought to you by Testkube—where teams run millions of performance tests in real Kubernetes infrastructure. From air-gapped environments to massive scale deployments, orchestrate every testing tool in one platform. Check it out at testkube.ioMore infoFind all the links and info for this episode here: https://ku.bz/Xvyp1_QcvInterested in sponsoring an episode? Learn more.
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The Double-Edged Sword of AI-Assisted Kubernetes Operations, with Mai Nishitani
Mai Nishitani, Director of Enterprise Architecture at NTT Data and AWS Community Builder, demonstrates how Model Context Protocol (MCP) enables Claude to directly interact with Kubernetes clusters through natural language commands.You will learn:How MCP servers work and why they're significant for standardizing AI integration with DevOps tools, moving beyond custom integrations to a universal protocolThe practical capabilities and critical limitations of AI in Kubernetes operationsWhy fundamental troubleshooting skills matter more than ever as AI abstractions can fail in unexpected ways, especially during crisis scenarios and complex system failuresHow DevOps roles are evolving from manual administration toward strategic architecture and orchestrationSponsorThis episode is brought to you by Testkube—where teams run millions of performance tests in real Kubernetes infrastructure. From air-gapped environments to massive scale deployments, orchestrate every testing tool in one platform. Check it out at testkube.ioMore infoFind all the links and info for this episode here: https://ku.bz/3hWvQjXxpInterested in sponsoring an episode? Learn more.
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The Making of Flux: The Future, a KubeFM Original Series
In this closing episode, Bryan Ross (Field CTO at GitLab), Jane Yan (Principal Program Manager at Microsoft), Sean O’Meara (CTO at Mirantis) and William Rizzo (Strategy Lead, CTO Office at Mirantis) discuss how GitOps evolves in practice.How enterprises are embedding Flux into developer platforms and managed cloud services.Why bridging CI/CD and infrastructure remains a core challenge—and how GitOps addresses it.What leading platform teams (GitLab, Microsoft, Mirantis) see as the next frontier for GitOps.SponsorJoin the Flux maintainers and community at FluxCon, November 11th in Atlanta—register hereMore infoFind all the links and info for this episode here: https://ku.bz/tVqKwNYQHInterested in sponsoring an episode? Learn more.
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The Data Engineer's guide to optimizing Kubernetes, with Niels Claeys
Niels Claeys shares how his team at Dataminded built Conveyor, a data platform processing up to 1.5 million core hours monthly. He explains the specific optimizations they discovered through production experience, from scheduler changes that immediately reduce costs by 10-15% to achieving 97% spot instance usage without reliability issues.You will learn:Why the default Kubernetes scheduler wastes money on batch workloads and how switching from "least allocated" to "most allocated" scheduling enables faster scale-down and better resource utilizationHow to achieve 97% spot instance adoption through strategic instance type diversification, region selection, and Spark-specific techniquesNode pool design principles that balance Kubernetes overhead with workload efficiencyPlatform-specific gotchas like AWS cross-AZ data transfer costs that can spike bills unexpectedlySponsorThis episode is brought to you by Testkube—where teams run millions of performance tests in real Kubernetes infrastructure. From air-gapped environments to massive scale deployments, orchestrate every testing tool in one platform. Check it out at testkube.ioMore infoFind all the links and info for this episode here: https://ku.bz/hGRfkzDJWInterested in sponsoring an episode? Learn more.
Discover all the great things happening in the world of Kubernetes, learn (controversial) opinions from the experts and explore the successes (and failures) of running Kubernetes at scale.
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