AWS CloudFormation vs Google Cloud Deployment Manager: 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, Google Cloud Deployment Manager is detailed as "Create and manage cloud resources with simple templates". Google Cloud Deployment Manager allows you to specify all the resources needed for your application in a declarative format using yaml.
AWS CloudFormation and Google Cloud Deployment Manager can be categorized as "Infrastructure Build" tools.
What is AWS CloudFormation?
What is Google Cloud Deployment Manager?
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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.
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
Context: I wanted to create an end to end IoT data pipeline simulation in Google Cloud IoT Core and other GCP services. I never touched Terraform meaningfully until working on this project, and it's one of the best explorations in my development career. The documentation and syntax is incredibly human-readable and friendly. I'm used to building infrastructure through the google apis via Python , but I'm so glad past Sung did not make that decision. I was tempted to use Google Cloud Deployment Manager, but the templates were a bit convoluted by first impression. I'm glad past Sung did not make this decision either.
Solution: Leveraging Google Cloud Build Google Cloud Run Google Cloud Bigtable Google BigQuery Google Cloud Storage Google Compute Engine along with some other fun tools, I can deploy over 40 GCP resources using Terraform!
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Check out the GitHub repo attached
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.
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.
Manage infrastructure as codes. Native AWS solution so it has better support to AWS resources than Terraform, also can leverage AWS Business Support.