As organizations modernize applications with microservices and Kubernetes, they often run into the same friction points: inconsistent service-to-service communication, complex integrations, uneven developer environments, and cloud-specific deployment differences that slow delivery and increase operational risk. Cloud-native promises portability and scale—but without the right patterns, teams end up rebuilding plumbing instead of delivering business value.
This session explores a pragmatic approach to building scalable, cloud-native microservices using Dapr as the distributed application runtime, combined with developer-friendly workflows like GitHub Codespaces to standardize environments and accelerate onboarding. We’ll start by framing the business case: how Dapr reduces “undifferentiated heavy lifting” (retries, service discovery patterns, pub/sub, secrets, state management) so teams can ship features faster while improving reliability and maintainability.
Drawing from hands-on implementation patterns, we’ll walk through an end-to-end reference architecture: decomposing services, introducing sidecar-based building blocks, enabling event-driven integration with pub/sub, and standardizing secure configuration and secrets across environments. We’ll then connect the developer experience to production by showing how a consistent Codespaces-based setup supports local development, CI pipelines, and repeatable deployments.
Finally, we’ll focus on multi-cloud portability: how to deploy the same application with minimal changes to AKS, EKS, or GKE, using Kubernetes-native manifests, consistent observability, and platform-agnostic runtime patterns. Real-world lessons learned will highlight common pitfalls (over-coupling, mis-scoped state, unclear boundaries, operational gaps) and how to avoid them.
This session is designed for architects, platform engineers, and senior developers. Attendees will leave with a clear blueprint for building scalable microservices with Dapr, improving developer productivity with Codespaces, and deploying confidently across major Kubernetes platforms with minimal effort.