AWS CloudFormation vs Google Cloud Deployment Manager

Overview

AWS CloudFormation

Stacks1.6K

Followers1.3K

Votes88

Google Cloud Deployment Manager

Stacks24

Followers113

Votes5

AWS CloudFormation vs Google Cloud Deployment Manager: What are the differences?

AWS CloudFormation and Google Cloud Deployment Manager are two popular infrastructure-as-code (IaC) tools used for managing cloud resources. Below are the key differences between these two services.

Cloud Provider Integration: One major difference between AWS CloudFormation and Google Cloud Deployment Manager is the cloud provider integration they offer. AWS CloudFormation is specific to AWS and integrates tightly with the AWS ecosystem, providing seamless resource provisioning and management within the AWS platform. On the other hand, Google Cloud Deployment Manager is designed specifically for Google Cloud Platform (GCP) and provides similar capabilities within the GCP environment.
Syntax and Configuration Language: AWS CloudFormation uses JSON or YAML templates as its syntax and configuration language. These templates define the desired state of the infrastructure, including resources, dependencies, and configurations. In contrast, Google Cloud Deployment Manager uses YAML or Python configurations to define and provision resources. This difference in syntax and configuration language allows users to choose their preferred format based on their familiarity and comfort.
Resource Coverage: Another significant difference lies in the extent of resource coverage provided by AWS CloudFormation and Google Cloud Deployment Manager. AWS CloudFormation offers broad coverage for AWS resources, including various EC2 instances, RDS databases, S3 buckets, and more. In contrast, Google Cloud Deployment Manager has a narrower coverage, focusing primarily on GCP resources like VM instances, Cloud Storage buckets, and Cloud SQL databases.
Template Reusability: AWS CloudFormation provides the concept of nested stacks, allowing users to reuse templates by referencing them within other templates. This enables modularization and reduces duplication of infrastructure code. Google Cloud Deployment Manager lacks a native feature for template reuse, although users can achieve similar functionality by separating resource configurations into reusable YAML or Python files.
Implementation Approach: AWS CloudFormation takes an imperative approach to infrastructure provisioning, where it largely relies on manual resource creation and configuration statements. In contrast, Google Cloud Deployment Manager follows a declarative approach, where users specify the desired state of the infrastructure, and the tool automatically handles resource creation and configuration. This difference in implementation can influence the overall user experience and preference.
Maturity and Ecosystem: AWS CloudFormation has been in the market for a longer time and has a mature ecosystem, with extensive community support, a rich library of pre-built templates, and a comprehensive documentation base. Google Cloud Deployment Manager, being a relatively newer service, has a growing ecosystem that is not as extensive as AWS CloudFormation. However, with the popularity of GCP increasing, the ecosystem is continuously expanding.

In summary, CloudFormation is an AWS service that uses declarative YAML or JSON templates for defining and provisioning AWS infrastructure, while Google Cloud Deployment Manager achieves similar goals on Google Cloud Platform using YAML or Python templates.

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Manual

Advice on AWS CloudFormation, Google Cloud Deployment Manager

Sung Won

Nov 4, 2019

Decidedon

Google Cloud IoT Core

Terraform

Python

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!

Check Out My Architecture: CLICK ME

Check out the GitHub repo attached

2.25M views2.25M

Comments

Timothy

SRE

Mar 20, 2020

Decided

I personally am not a huge fan of vendor lock in for multiple reasons:

I've seen cost saving moves to the cloud end up costing a fortune and trapping companies due to over utilization of cloud specific features.
I've seen S3 failures nearly take down half the internet.
I've seen companies get stuck in the cloud because they aren't built cloud agnostic.

I choose to use terraform for my cloud provisioning for these reasons:

It's cloud agnostic so I can use it no matter where I am.
It isn't difficult to use and uses a relatively easy to read language.
It tests infrastructure before running it, and enables me to see and keep changes up to date.
It runs from the same CLI I do most of my CM work from.

385k views385k

Comments

Daniel

May 4, 2020

Decided

Because Pulumi uses real programming languages, you can actually write abstractions for your infrastructure code, which is incredibly empowering. You still 'describe' your desired state, but by having a programming language at your fingers, you can factor out patterns, and package it up for easier consumption.

426k views426k

Comments

Detailed Comparison

AWS CloudFormation	Google Cloud Deployment Manager
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.	Google Cloud Deployment Manager allows you to specify all the resources needed for your application in a declarative format using yaml.
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.	-
Statistics
Stacks 1.6K	Stacks 24
Followers 1.3K	Followers 113
Votes 88	Votes 5
Pros & Cons
Pros 43 Automates infrastructure deployments 21 Declarative infrastructure and deployment 13 No more clicking around 3 Any Operative System you want 3 Infrastructure as code Cons 4 Brittle 2 No RBAC and policies in templates	Pros 2 Automates infrastructure deployments 1 Infrastracture as a code 1 Easy to deploy for GCP 1 Fast deploy and update Cons 1 Only using in GCP
Integrations
No integrations available	Jinja Python Google Cloud Storage Google Compute Engine Google Cloud SQL

What are some alternatives to AWS CloudFormation, Google Cloud Deployment Manager?

Packer

Packer automates the creation of any type of machine image. It embraces modern configuration management by encouraging you to use automated scripts to install and configure the software within your Packer-made images.

Scalr

Scalr is a remote state & operations backend for Terraform with access controls, policy as code, and many quality of life features.

Pulumi

Pulumi is a cloud development platform that makes creating cloud programs easy and productive. Skip the YAML and just write code. Pulumi is multi-language, multi-cloud and fully extensible in both its engine and ecosystem of packages.

Azure Resource Manager

It is the deployment and management service for Azure. It provides a management layer that enables you to create, update, and delete resources in your Azure subscription. You use management features, like access control, locks, and tags, to secure and organize your resources after deployment.

Habitat

Habitat is a new approach to automation that focuses on the application instead of the infrastructure it runs on. With Habitat, the apps you build, deploy, and manage behave consistently in any runtime — metal, VMs, containers, and PaaS. You'll spend less time on the environment and more time building features.

AWS Cloud Development Kit

It is an open source software development framework to model and provision your cloud application resources using familiar programming languages. It uses the familiarity and expressive power of programming languages for modeling your applications. It provides you with high-level components that preconfigure cloud resources with proven defaults, so you can build cloud applications without needing to be an expert.

Yocto

It is an open source collaboration project that helps developers create custom Linux-based systems regardless of the hardware architecture. It provides a flexible set of tools and a space where embedded developers worldwide can share technologies, software stacks, configurations, and best practices that can be used to create tailored Linux images for embedded and IOT devices, or anywhere a customized Linux OS is needed.

GeoEngineer

GeoEngineer uses Terraform to plan and execute changes, so the DSL to describe resources is similar to Terraform's. GeoEngineer's DSL also provides programming and object oriented features like inheritance, abstraction, branching and looping.

Atlas

Atlas is one foundation to manage and provide visibility to your servers, containers, VMs, configuration management, service discovery, and additional operations services.

Buildroot

It is a tool that simplifies and automates the process of building a complete Linux system for an embedded system, using cross-compilation.

Related Comparisons

AWS CloudFormation vs Google Cloud Deployment Manager: What are the differences?

AWS CloudFormation and Google Cloud Deployment Manager are two popular infrastructure-as-code (IaC) tools used for managing cloud resources. Below are the key differences between these two services.

Cloud Provider Integration: One major difference between AWS CloudFormation and Google Cloud Deployment Manager is the cloud provider integration they offer. AWS CloudFormation is specific to AWS and integrates tightly with the AWS ecosystem, providing seamless resource provisioning and management within the AWS platform. On the other hand, Google Cloud Deployment Manager is designed specifically for Google Cloud Platform (GCP) and provides similar capabilities within the GCP environment.
Syntax and Configuration Language: AWS CloudFormation uses JSON or YAML templates as its syntax and configuration language. These templates define the desired state of the infrastructure, including resources, dependencies, and configurations. In contrast, Google Cloud Deployment Manager uses YAML or Python configurations to define and provision resources. This difference in syntax and configuration language allows users to choose their preferred format based on their familiarity and comfort.
Resource Coverage: Another significant difference lies in the extent of resource coverage provided by AWS CloudFormation and Google Cloud Deployment Manager. AWS CloudFormation offers broad coverage for AWS resources, including various EC2 instances, RDS databases, S3 buckets, and more. In contrast, Google Cloud Deployment Manager has a narrower coverage, focusing primarily on GCP resources like VM instances, Cloud Storage buckets, and Cloud SQL databases.
Template Reusability: AWS CloudFormation provides the concept of nested stacks, allowing users to reuse templates by referencing them within other templates. This enables modularization and reduces duplication of infrastructure code. Google Cloud Deployment Manager lacks a native feature for template reuse, although users can achieve similar functionality by separating resource configurations into reusable YAML or Python files.
Implementation Approach: AWS CloudFormation takes an imperative approach to infrastructure provisioning, where it largely relies on manual resource creation and configuration statements. In contrast, Google Cloud Deployment Manager follows a declarative approach, where users specify the desired state of the infrastructure, and the tool automatically handles resource creation and configuration. This difference in implementation can influence the overall user experience and preference.
Maturity and Ecosystem: AWS CloudFormation has been in the market for a longer time and has a mature ecosystem, with extensive community support, a rich library of pre-built templates, and a comprehensive documentation base. Google Cloud Deployment Manager, being a relatively newer service, has a growing ecosystem that is not as extensive as AWS CloudFormation. However, with the popularity of GCP increasing, the ecosystem is continuously expanding.

AWS CloudFormation vs Google Cloud Deployment Manager

Overview