AWS CloudFormation vs Kubernetes

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AWS CloudFormation vs Kubernetes: What are the differences?

Developers describe AWS CloudFormation as "Create and manage a collection of related AWS resources". You can use AWS CloudFormation’s sample templates or create your own templates to describe the AWS resources, and any associated dependencies or runtime parameters, required to run your application. You don’t need to figure out the order in which AWS services need to be provisioned or the subtleties of how to make those dependencies work. On the other hand, Kubernetes is detailed as "Manage a cluster of Linux containers as a single system to accelerate Dev and simplify Ops". Kubernetes is an open source orchestration system for Docker containers. It handles scheduling onto nodes in a compute cluster and actively manages workloads to ensure that their state matches the users declared intentions.

AWS CloudFormation can be classified as a tool in the "Infrastructure Build Tools" category, while Kubernetes is grouped under "Container Tools".

Some of the features offered by AWS CloudFormation are:

  • AWS CloudFormation comes with the following ready-to-run sample templates: WordPress (blog),Tracks (project tracking), Gollum (wiki used by GitHub), Drupal (content management), Joomla (content management), Insoshi (social apps), Redmine (project mgmt)
  • No Need to Reinvent the Wheel – A template can be used repeatedly to create identical copies of the same stack (or to use as a foundation to start a new stack)
  • Transparent and Open – Templates are simple JSON formatted text files that can be placed under your normal source control mechanisms, stored in private or public locations such as Amazon S3 and exchanged via email.

On the other hand, Kubernetes provides the following key features:

  • Lightweight, simple and accessible
  • Built for a multi-cloud world, public, private or hybrid
  • Highly modular, designed so that all of its components are easily swappable

"Automates infrastructure deployments" is the top reason why over 36 developers like AWS CloudFormation, while over 134 developers mention "Leading docker container management solution" as the leading cause for choosing Kubernetes.

Kubernetes is an open source tool with 55K GitHub stars and 19.1K GitHub forks. Here's a link to Kubernetes's open source repository on GitHub.

Google, Slack, and Shopify are some of the popular companies that use Kubernetes, whereas AWS CloudFormation is used by Expedia.com, Redox Engine, and TimeHop. Kubernetes has a broader approval, being mentioned in 1046 company stacks & 1096 developers stacks; compared to AWS CloudFormation, which is listed in 197 company stacks and 77 developer stacks.

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What is AWS CloudFormation?

You can use AWS CloudFormation’s sample templates or create your own templates to describe the AWS resources, and any associated dependencies or runtime parameters, required to run your application. You don’t need to figure out the order in which AWS services need to be provisioned or the subtleties of how to make those dependencies work.

What is Kubernetes?

Kubernetes is an open source orchestration system for Docker containers. It handles scheduling onto nodes in a compute cluster and actively manages workloads to ensure that their state matches the users declared intentions.
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Why do developers choose AWS CloudFormation?
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What are some alternatives to AWS CloudFormation and Kubernetes?
AWS CodeDeploy
AWS CodeDeploy is a service that automates code deployments to Amazon EC2 instances. AWS CodeDeploy makes it easier for you to rapidly release new features, helps you avoid downtime during deployment, and handles the complexity of updating your applications.
Chef
Chef enables you to manage and scale cloud infrastructure with no downtime or interruptions. Freely move applications and configurations from one cloud to another. Chef is integrated with all major cloud providers including Amazon EC2, VMWare, IBM Smartcloud, Rackspace, OpenStack, Windows Azure, HP Cloud, Google Compute Engine, Joyent Cloud and others.
Terraform
With Terraform, you describe your complete infrastructure as code, even as it spans multiple service providers. Your servers may come from AWS, your DNS may come from CloudFlare, and your database may come from Heroku. Terraform will build all these resources across all these providers in parallel.
AWS Elastic Beanstalk
Once you upload your application, Elastic Beanstalk automatically handles the deployment details of capacity provisioning, load balancing, auto-scaling, and application health monitoring.
AWS Config
AWS Config is a fully managed service that provides you with an AWS resource inventory, configuration history, and configuration change notifications to enable security and governance. With AWS Config you can discover existing AWS resources, export a complete inventory of your AWS resources with all configuration details, and determine how a resource was configured at any point in time. These capabilities enable compliance auditing, security analysis, resource change tracking, and troubleshooting.
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Decisions about AWS CloudFormation and Kubernetes
Yshay Yaacobi
Yshay Yaacobi
Software Engineer · | 27 upvotes · 271.9K views
atSolutoSoluto
Docker Swarm
Docker Swarm
Kubernetes
Kubernetes
Visual Studio Code
Visual Studio Code
Go
Go
TypeScript
TypeScript
JavaScript
JavaScript
C#
C#
F#
F#
.NET
.NET

Our first experience with .NET core was when we developed our OSS feature management platform - Tweek (https://github.com/soluto/tweek). We wanted to create a solution that is able to run anywhere (super important for OSS), has excellent performance characteristics and can fit in a multi-container architecture. We decided to implement our rule engine processor in F# , our main service was implemented in C# and other components were built using JavaScript / TypeScript and Go.

Visual Studio Code worked really well for us as well, it worked well with all our polyglot services and the .Net core integration had great cross-platform developer experience (to be fair, F# was a bit trickier) - actually, each of our team members used a different OS (Ubuntu, macos, windows). Our production deployment ran for a time on Docker Swarm until we've decided to adopt Kubernetes with almost seamless migration process.

After our positive experience of running .Net core workloads in containers and developing Tweek's .Net services on non-windows machines, C# had gained back some of its popularity (originally lost to Node.js), and other teams have been using it for developing microservices, k8s sidecars (like https://github.com/Soluto/airbag), cli tools, serverless functions and other projects...

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Sebastian Gębski
Sebastian Gębski
CTO at Shedul/Fresha · | 6 upvotes · 48.5K views
atFresha EngineeringFresha Engineering
Amazon RDS
Amazon RDS
Amazon S3
Amazon S3
Amazon EKS
Amazon EKS
Amazon EC2
Amazon EC2
Ansible
Ansible
Terraform
Terraform
Kubernetes
Kubernetes
Docker Compose
Docker Compose
Docker
Docker

Heroku was a decent choice to start a business, but at some point our platform was too big, too complex & too heterogenic, so Heroku started to be a constraint, not a benefit. First, we've started containerizing our apps with Docker to eliminate "works in my machine" syndrome & uniformize the environment setup. The first orchestration was composed with Docker Compose , but at some point it made sense to move it to Kubernetes. Fortunately, we've made a very good technical decision when starting our work with containers - all the container configuration & provisions HAD (since the beginning) to be done in code (Infrastructure as Code) - we've used Terraform & Ansible for that (correspondingly). This general trend of containerisation was accompanied by another, parallel & equally big project: migrating environments from Heroku to AWS: using Amazon EC2 , Amazon EKS, Amazon S3 & Amazon RDS.

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Emanuel Evans
Emanuel Evans
Senior Architect at Rainforest QA · | 12 upvotes · 123.7K views
atRainforest QARainforest QA
Terraform
Terraform
Helm
Helm
Google Cloud Build
Google Cloud Build
CircleCI
CircleCI
Redis
Redis
Google Cloud Memorystore
Google Cloud Memorystore
PostgreSQL
PostgreSQL
Google Cloud SQL for PostgreSQL
Google Cloud SQL for PostgreSQL
Google Kubernetes Engine
Google Kubernetes Engine
Kubernetes
Kubernetes
Heroku
Heroku

We recently moved our main applications from Heroku to Kubernetes . The 3 main driving factors behind the switch were scalability (database size limits), security (the inability to set up PostgreSQL instances in private networks), and costs (GCP is cheaper for raw computing resources).

We prefer using managed services, so we are using Google Kubernetes Engine with Google Cloud SQL for PostgreSQL for our PostgreSQL databases and Google Cloud Memorystore for Redis . For our CI/CD pipeline, we are using CircleCI and Google Cloud Build to deploy applications managed with Helm . The new infrastructure is managed with Terraform .

Read the blog post to go more in depth.

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Joseph Kunzler
Joseph Kunzler
DevOps Engineer at Tillable · | 9 upvotes · 30.4K views
atTillableTillable
AWS CloudFormation
AWS CloudFormation
AWS Elastic Load Balancing (ELB)
AWS Elastic Load Balancing (ELB)
Amazon EC2
Amazon EC2
Amazon S3
Amazon S3
Terraform
Terraform

We use Terraform because we needed a way to automate the process of building and deploying feature branches. We wanted to hide the complexity such that when a dev creates a PR, it triggers a build and deployment without the dev having to worry about any of the 'plumbing' going on behind the scenes. Terraform allows us to automate the process of provisioning DNS records, Amazon S3 buckets, Amazon EC2 instances and AWS Elastic Load Balancing (ELB)'s. It also makes it easy to tear it all down when finished. We also like that it supports multiple clouds, which is why we chose to use it over AWS CloudFormation.

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GitHub
GitHub
nginx
nginx
ESLint
ESLint
AVA
AVA
Semantic UI React
Semantic UI React
Redux
Redux
React
React
PostgreSQL
PostgreSQL
ExpressJS
ExpressJS
Node.js
Node.js
FeathersJS
FeathersJS
Heroku
Heroku
Amazon EC2
Amazon EC2
Kubernetes
Kubernetes
Jenkins
Jenkins
Docker Compose
Docker Compose
Docker
Docker
#Frontend
#Stack
#Backend
#Containers
#Containerized

Recently I have been working on an open source stack to help people consolidate their personal health data in a single database so that AI and analytics apps can be run against it to find personalized treatments. We chose to go with a #containerized approach leveraging Docker #containers with a local development environment setup with Docker Compose and nginx for container routing. For the production environment we chose to pull code from GitHub and build/push images using Jenkins and using Kubernetes to deploy to Amazon EC2.

We also implemented a dashboard app to handle user authentication/authorization, as well as a custom SSO server that runs on Heroku which allows experts to easily visit more than one instance without having to login repeatedly. The #Backend was implemented using my favorite #Stack which consists of FeathersJS on top of Node.js and ExpressJS with PostgreSQL as the main database. The #Frontend was implemented using React, Redux.js, Semantic UI React and the FeathersJS client. Though testing was light on this project, we chose to use AVA as well as ESLint to keep the codebase clean and consistent.

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AWS CloudFormation
AWS CloudFormation
Google Cloud Deployment Manager
Google Cloud Deployment Manager
Terraform
Terraform

I use Terraform because it hits the level of abstraction pocket of being high-level and flexible, and is agnostic to cloud platforms. Creating complex infrastructure components for a solution with a UI console is tedious to repeat. Using low-level APIs are usually specific to cloud platforms, and you still have to build your own tooling for deploying, state management, and destroying infrastructure.

However, Terraform is usually slower to implement new services compared to cloud-specific APIs. It's worth the trade-off though, especially if you're multi-cloud. I heard someone say, "We want to preference a cloud, not lock in to one." Terraform builds on that claim.

Terraform Google Cloud Deployment Manager AWS CloudFormation

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Ido Shamun
Ido Shamun
at The Elegant Monkeys · | 6 upvotes · 44.4K views
atDailyDaily
Helm
Helm
Docker
Docker
CircleCI
CircleCI
GitHub
GitHub
Kubernetes
Kubernetes

Kubernetes powers our #backend services as it is very easy in terms of #devops (the managed version). We deploy everything using @helm charts as it provides us to manage deployments the same way we manage our code on GitHub . On every commit a CircleCI job is triggered to run the tests, build Docker images and deploy them to the registry. Finally on every master commit CircleCI also deploys the relevant service using Helm chart to our Kubernetes cluster

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Russel Werner
Russel Werner
Lead Engineer at StackShare · | 0 upvotes · 5K views
atStackShareStackShare
Amazon EC2 Container Service
Amazon EC2 Container Service
CircleCI
CircleCI
Helm
Helm
Slack
Slack
Google Kubernetes Engine
Google Kubernetes Engine
Amazon EKS
Amazon EKS
Kubernetes
Kubernetes
Heroku
Heroku

We began our hosting journey, as many do, on Heroku because they make it easy to deploy your application and automate some of the routine tasks associated with deployments, etc. However, as our team grew and our product matured, our needs have outgrown Heroku. I will dive into the history and reasons for this in a future blog post.

We decided to migrate our infrastructure to Kubernetes running on Amazon EKS. Although Google Kubernetes Engine has a slightly more mature Kubernetes offering and is more user-friendly; we decided to go with EKS because we already using other AWS services (including a previous migration from Heroku Postgres to AWS RDS). We are still in the process of moving our main website workloads to EKS, however we have successfully migrate all our staging and testing PR apps to run in a staging cluster. We developed a Slack chatops application (also running in the cluster) which automates all the common tasks of spinning up and managing a production-like cluster for a pull request. This allows our engineering team to iterate quickly and safely test code in a full production environment. Helm plays a central role when deploying our staging apps into the cluster. We use CircleCI to build docker containers for each PR push, which are then published to Amazon EC2 Container Service (ECR). An upgrade-operator process watches the ECR repository for new containers and then uses Helm to rollout updates to the staging environments. All this happens automatically and makes it really easy for developers to get code onto servers quickly. The immutable and isolated nature of our staging environments means that we can do anything we want in that environment and quickly re-create or restore the environment to start over.

The next step in our journey is to migrate our production workloads to an EKS cluster and build out the CD workflows to get our containers promoted to that cluster after our QA testing is complete in our staging environments.

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Robert Zuber
Robert Zuber
CTO at CircleCI · | 6 upvotes · 13.4K views
atCircleCICircleCI
Helm
Helm
Nomad
Nomad
Kubernetes
Kubernetes
Docker
Docker

Our backend consists of two major pools of machines. One pool hosts the systems that run our site, manage jobs, and send notifications. These services are deployed within Docker containers orchestrated in Kubernetes. Due to Kubernetes’ ecosystem and toolchain, it was an obvious choice for our fairly statically-defined processes: the rate of change of job types or how many we may need in our internal stack is relatively low.

The other pool of machines is for running our users’ jobs. Because we cannot dynamically predict demand, what types of jobs our users need to have run, nor the resources required for each of those jobs, we found that Nomad excelled over Kubernetes in this area.

We’re also using Helm to make it easier to deploy new services into Kubernetes. We create a chart (i.e. package) for each service. This lets us easily roll back new software and gives us an audit trail of what was installed or upgraded.

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Interest over time
Reviews of AWS CloudFormation and Kubernetes
Review ofKubernetesKubernetes

It's a little bit complex to onboard, but once you grasp all the different concepts the platform is really powerful, and infrastructure stops being an issue.

Service discovery, auto-recovery, scaling and orchestration are just a few of the features you get.

How developers use AWS CloudFormation and Kubernetes
Avatar of CloudRepo
CloudRepo uses AWS CloudFormationAWS CloudFormation

Manually clicking around the AWS UI or scripting AWS CLI calls can be both a slow and brittle process.

We needed to be able to reconstruct CloudRepo's infrastructure in case of disaster or moving to another AWS Region.

Setting up our infrastructure with CloudFormation allows us to update it easily as well as duplicate or recreate things when the need arises.

Avatar of Matt Welke
Matt Welke uses KubernetesKubernetes

Just tinkering with it for personal use at this stage based on positive experience using it at work. Plan to use it for high traffic distributed systems if not using a managed hosting service like Heroku, AWS Lambda, or Google Cloud Functions. Reasons for using instead of these alternatives would be cheaper cost at higher scale.

Avatar of Opstax Ltd
Opstax Ltd uses AWS CloudFormationAWS CloudFormation

Opstax uses CloudFormation for anything infrastructure related! CloudFormation allows us to use infrastructure-as-code as a constant blueprint/map of our environment. It means we can accurately and efficiently deploy replicated or new infrastructure with no time wasted clicking around and no human error.

Avatar of realcloudratics
realcloudratics uses KubernetesKubernetes

Good existential question. Kubernetes is painful in the extreme - especially when combined with Ansible. The layers of indirection are truly mind altering. But hey - containers are kewl!

Avatar of Japan Digital Design
Japan Digital Design uses KubernetesKubernetes

Our developer experience system is on Kubernetes (Google Kubernetes Engine at the moment). We would like to expand our Kubernetes clusters over other Kubernetes engine.

Avatar of Flux Work
Flux Work uses AWS CloudFormationAWS CloudFormation

Manage infrastructure as codes. Native AWS solution so it has better support to AWS resources than Terraform, also can leverage AWS Business Support.

Avatar of ShareThis
ShareThis uses KubernetesKubernetes

Kubernetes is used for managing microclusters within our AWS infrastructure. This allows us to deploy new infrastructure in seconds.

Avatar of papaver
papaver uses KubernetesKubernetes

minor experience with kubernetes. helped a client setup a kubernetes infrastructure. love the elegance of the system.

Avatar of Endource
Endource uses AWS CloudFormationAWS CloudFormation

Manages the infrastructure for core website

Avatar of Patty R
Patty R uses AWS CloudFormationAWS CloudFormation

Deploys and maintains the infrastructure.

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