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

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cost-benefit analysis to make sure the tool's scability, reliability
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and related functionality will outweigh the downsides.
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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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* [How Zalando 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.
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‎content/pages/05-deployment/38-serverless.markdown‎

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and not all of them have Python support.
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* [AWS Lambda](/aws-lambda.html) is the current leader among serverless
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compute implementations. It has support for both
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[Python 2.7](/blog/aws-lambda-python-2-7.html) and
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[Python 3.6/3.7](/blog/aws-lambda-python-3-6.html).
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compute implementations. It has support for
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[Python 3.x](/blog/aws-lambda-python-3-6.html).
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* Azure Functions has second-class citizen support for Python. It's
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supposed to be possible but
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* [Google Cloud Functions](/google-cloud-functions.html) currently
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only supports JavaScript code execution.
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* Webtask.io also only supports JavaScript but there is a cool
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*prototype* project named [webtask-pytask](https://github.com/tehsis/webtask-pytask)
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to run Python code in the browser via webtask. This demo is definitely not
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for production code use but awesome to see what the programming community
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can put together using existing code and services.
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* [Webtask.io](https://webtask.io/) started as a JavaScript service but
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now also has a Python runtime as well.
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### Serverless frameworks
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([source code](https://github.com/aws/chalice)) is built by the AWS team
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specifically for Python applications.
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* [Apex](http://apex.run/) ([source code](https://github.com/apex/apex))
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### General serverless resources
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Serverless concepts and implementations are still in their early
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There is some concern by organizations and developers about vendor lock-in
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on serverless platforms. It is unclear if portability is worse for
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serverless than other infrastructure-as-a-service pieces, but still worth
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thinking about ahead of time. These resources provide additional
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perspectives on lock-in and using multiple cloud providers.
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* [On Serverless, Multi-Cloud, and Vendor Lock In](https://blog.symphonia.io/on-serverless-multi-cloud-and-vendor-lock-in-da930b3993f)
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is an opinion piece that for *most* cases the additional work of
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going multi-cloud is not worth the tradeoffs, therefore at this time
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it's better to go for a single vendor such as AWS or Azure and optimize
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on that platform.
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* [Why vendor lock-in with serverless isn’t what you think it is](https://medium.com/@PaulDJohnston/why-vendor-lock-in-with-serverless-isnt-what-you-think-it-is-d6be40fa9ca9)
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recommends using a single vendor for now and stop worrying about
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hedging your bets because it typically makes your infrastructure
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significantly more complex.
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thinking about ahead of time.
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[Why vendor lock-in with serverless isn’t what you think it is](https://medium.com/@PaulDJohnston/why-vendor-lock-in-with-serverless-isnt-what-you-think-it-is-d6be40fa9ca9)
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is a piece on this topic that recommends using a single vendor for
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now and for organizations to stop worrying about hedging their bets
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because it typically makes infrastructure significantly more complex.

‎content/pages/06-devops/01-monitoring.markdown‎

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* [New Relic](http://newrelic.com/) provides application and database
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monitoring as well as plug ins for capturing and analyzing data about
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other devleoper tools in your stack, such as [Twilio](/twilio.html).
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other developer tools in your stack, such as [Twilio](/twilio.html).
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* [Opbeat](https://opbeat.com) Built for django. Opbeat combines performance
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metrics, release tracking, and error logging into a single simple service.
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* [Scout](https://scoutapp.com/python-monitoring) monitors the performance of Django and Flask apps, auto-instrumenting views, SQL queries, templates, and more.
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* [The Virtues of Monitoring](http://www.paperplanes.de/2011/1/5/the_virtues_of_monitoring.html)
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* [Effortless Monitoring with collectd, Graphite, and Docker](http://blog.docker.io/2013/07/effortless-monitoring-with-collectd-graphite-and-docker/)
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* [Practical Guide to StatsD/Graphite Monitoring](http://matt.aimonetti.net/posts/2013/06/26/practical-guide-to-graphite-monitoring/)
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is a detailed guide with code examples for monitoring infrastructure.
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* Bit.ly describes the
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"[10 Things They Forgot to Monitor](http://word.bitly.com/post/74839060954/ten-things-to-monitor)"
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beyond the standard metrics such as disk & memory usage.

‎content/pages/06-devops/18-web-analytics.markdown‎

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is not for everyone but it is really useful if you want to avoid the Google
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data trap.
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* This beginner's guide to
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[math and stats behind web analytics](http://www.seotakeaways.com/beginners-guide-maths-stats-web-analytics/)
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provides some context for understanding and reasoning about web traffic.
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* [An Analytics Primer for Developers](https://hacks.mozilla.org/2015/03/an-analytics-primer-for-developers/)
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by Mozilla explains what to track, choosing an analytics platform and how
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to serve up the analytics JavaScript asynchronously.

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