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new kubernetes and code metrics resources
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content/pages/05-deployment/00-deployment.markdown

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### Deployment resources
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* If you need a step-by-step guide to deploying a Python web application,
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I wrote [a whole book](http://www.deploypython.com/) on exactly this topic
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called
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[The Full Stack Python Guide to Deployments](https://gumroad.com/l/WOvyt)
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that you'll find super helpful.
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* [Hello, production](https://blog.thepete.net/blog/2019/10/04/hello-production/)
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lays out the powerful philosophy of putting a project into production as
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soon as possible in a project's lifecycle to establish the pipeline,
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identify issues and bottlenecks, and build the foundation for continuous
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delivery. The post also covers common objections and provides some
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arguments to help you convince others that this strategy is the right
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way to go on all projects.
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* [Deploying Python web applications](http://talkpython.fm/episodes/show/26/deploying-python-web-applications-updated)
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is an episode of the great Talk Python to Me podcast series where I

content/pages/05-deployment/37-kubernetes.markdown

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uses Helm to make it easier to deploy the example [Django](/django.html)
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web app with a [PostgreSQL](/postgresql.html) backend.
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* [How Zolando manages 140+ Kubernetes clusters](https://srcco.de/posts/how-zalando-manages-140-kubernetes-clusters.html)
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covers the architecture, monitoring and workflow of a team that has
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to run a decent number of clusters for their development teams.

content/pages/06-devops/02-prometheus.markdown

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deploys the [Django](/django.html) web app using
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[Helm](https://helm.sh/) and configures Prometheus to scrape metrics
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from the application running on Kubernetes.
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* [A gentle introduction to the wonderful world of metrics](https://tech.showmax.com/2019/10/prometheus-introduction/)
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has a quick summary that compares Prometheus with Nagios, then digs
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into the logging format and what you can visualize with this tool.

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