Auf den devopsdays 2015 in Berlin hatten wir eine Open Space Gruppe, die sich mit dem Thema beschäftigte: “Ich lese viele Bücher, Blogs etc, und würde dieses Wissen gerne in das Unternehmen tragen, in dem ich gerade arbeite”. Dabei kam die Idee auf, Lesegruppen zu bilden. Diese Idee fand ich so gut, dass ich sie direkt einmal ausprobiert habe.
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Elastic Beanstalk is a great AWS service for managed application hosting. For me personally, it’s the Heroku of AWS: Developers can concentrate on developing their application while AWS takes care of all the heavy lifting of scaling, deployment, runtime updates, monitoring, logging etcpp.
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When deploying CloudFormation stacks in a “Continuous Delivery” manner with CodePipeline, one might encounter the challenge to pass many parameters from the CloudFormation stack describing the pipeline to another stack describing the infrastructure to be deployed (in this example a stack named application).
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Update 2019-01-24: CloudFormation now natively supports a update policy for replacments with the UpdateReplacePolicy.
Some operations in CloudFormation trigger a REPLACEMENT of resources which can have unintended and catastrophic consequences, e.g. an RDS instance being replaced (which means that the current database will be deleted by CloudFormation after a new one has been created).
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When developing CloudFormation templates, I regularly missed an idempotent one-liner command which does something like “create or update stack N with these parameters”, which provides a fast feedback loop.
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This article explores how to build low-maintenance Continuous Delivery pipelines for Terraform, by using AWS building blocks CloudFormation, CodePipeline and CodeBuild.
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This is a follow-up of my AWSAdvent article Serverless everything: One-button serverless deployment pipeline for a serverless app , which extends the example deployment pipeline with AWS CodeBuild.
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Over the past days I moved my blog over to AWS CloudFront and S3, powered by the static blog generator Hexo.
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I’ve been using CloudFormation YAML syntax for a while now with Ansible and the serverless framework which would convert the YAML to JSON before uploading the template. That already gave me the YAML advantages of e.g. code comments, not having to care about commas etc.
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My current experiment is to build a serverless deploy pipeline (With AWS CodePipeline) which uses AWS Lambda for the build steps. One step includes to invoke NPM to build a static website out of JavaScript components (which would be deployed to an S3 bucket in a later step).
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