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Scaling Kubernetes, Microservices, and Ephemeral Environments
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Speedscale addresses the challenges of scaling Kubernetes in a microservices and containerized, ephemeral environment. This includes real-traffic replays and service mocking to find bottlenecks and tune production and development environments.
This episode sponsored by SpeedScale https://bit.ly/46KFWbY
Insights on Scaling Kubernetes
🔍 Speedcale helps developers figure out if their code is about to blow up before pushing it into production by creating production conditions in their staging environments and local development machines.
🌐 Kubernetes enables teams to build microservice architectures, breaking the monolith into pieces and allowing for individual scaling of each component.
🚀 The ability to make small code changes and quickly push them to production with Kubernetes provides a time to market advantage for companies.
🚀 Speed and scale are key capabilities for teams testing their code in Kubernetes environments, not just for simulating production.
📊 Monitoring data and load testing are crucial for defining the memory and CPU needs of workloads in Kubernetes environments.
🚀 Scaling Kubernetes clusters can be challenging, but innovations like Carpenter can help manage node sizing and resource allocation effectively.
🔍 Using production monitoring data from tools like New Relic and DataDog can help in tuning production and non-production environments for Kubernetes and microservices.
🔮 Mocking out dependencies with one command line tool can revolutionize the development process and improve developer satisfaction.
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