What is AWS CloudFormation?
Who uses AWS CloudFormation?
AWS CloudFormation Integrations
Here are some stack decisions, common use cases and reviews by companies and developers who chose AWS CloudFormation in their tech stack.
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.
Yesterday we moved away from using CloudFlare towards Amazon Route 53 for a few reasons. Although CloudFlare is a great platform, once you reach almost a 100% AWS Service integration, it makes it hard to still use CloudFlare in the stack. Also being able to use Aliases for DNS makes it faster because instead of doing a CNAME and an A record lookup, you will be able to receive the A records from the end services directly. We always loved working with CloudFlare , especially for DNS as we already used Amazon CloudFront for CDN. But having everything within AWS makes it "cleaner" when deploying automatically using AWS CloudFormation. All that aside, the main reason for moving towards Amazon Route 53 for DNS is the ability to do geolocation and latency based DNS responses. Doing this outside the AWS console would increase the complexity.
Working on a project recently, wanted an easy modern frontend to work with, decoupled from our backend. To get things going quickly, decided to go with React, Redux.js, redux-saga, Bootstrap.
On the backend side, Go is a personal favourite, and wanted to minimize server overheads so went with a #serverless architecture leveraging AWS Lambda, AWS CloudFormation, Amazon DynamoDB, etc.
For IDE/tooling I tend to stick to the #JetBrains tools: WebStorm / Goland.
Obviously using Git, with GitLab private repo's for managing code/issues/etc.
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
We use AWS CloudFormation because we needed a way to quickly and efficiently create & deploy lambda functions. We found two frameworks that work nicely as 'syntactic sugar' on top of CloudFormation - One of them being AWS SAM, and the other being Serverless Framework. We currently use the Serverless Framework because of it's multi-cloud capabilities.
At Qrvey we moved from a SaaS application running in AWS to a deployed model where we would deploy the complete infrastructure and code to a customer's AWS account. This created a unique challenge as we were Cloud Native and hence were using a lot of AWS Services like Amazon DynamoDB, AWS Fargate , Amazon Elasticsearch Service, etc. We decided to first build AWS CloudFormation templates to convert all our infrastructure into code. Then created a AWS CloudFormation template that would first generate a AWS CodePipeline into a customer's AWS account. This pipeline would then deploy our Infrastructure AWS CloudFormation template and the code on that Infrastructure. This simplified and completely automated our upgrade process as well.
AWS CloudFormation's Features
- 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.
- Declarative and Flexible – To create the infrastructure you want, you enumerate what AWS resources, configuration values and interconnections you need in a template and then let AWS CloudFormation do the rest with a few simple clicks in the AWS Management Console, via the command line tools or by calling the APIs.