Overview

Azure Resource Manager

Stacks42

Followers93

Votes11

GitHub Stars64

Forks47

Google Cloud Deployment Manager

Stacks26

Followers113

Votes5

Azure Resource Manager vs Google Cloud Deployment Manager: What are the differences?

Azure Resource Manager (ARM) and Google Cloud Deployment Manager (CDM) are both infrastructure-as-code (IaC) tools that allow users to define and manage cloud resources in a declarative way. While they have similar functionalities, there are several key differences between ARM and CDM.

Multicloud Support: Azure Resource Manager is specific to the Azure cloud, while Google Cloud Deployment Manager is designed for the Google Cloud Platform. This means that ARM can only be used to manage resources in Azure, while CDM supports multiple cloud providers. CDM can be used to manage resources across Google Cloud, AWS, and even on-premises systems.
Language and Syntax: Azure Resource Manager uses JSON (JavaScript Object Notation) for defining resource templates, which can be complex and challenging for beginners to understand. On the other hand, Google Cloud Deployment Manager uses YAML (YAML Ain't Markup Language), which is considered more human-readable and easier to work with.
Deployment and Updates: In Azure Resource Manager, resources can be deployed and updated as a single unit known as a resource group. This can be advantageous when managing related resources together. In Google Cloud Deployment Manager, resources are deployed and updated individually, which provides more flexibility but requires extra management overhead.
Resource Types: Azure Resource Manager supports a wide range of resource types specific to the Azure platform, including virtual machines, storage accounts, and virtual networks. Google Cloud Deployment Manager has its own set of resource types, such as compute instances, storage buckets, and networking resources. The resource types and their capabilities may vary between the two tools.
Integration with Other Services: Azure Resource Manager integrates tightly with other Azure services, such as Azure Active Directory for authentication and authorization. Google Cloud Deployment Manager similarly integrates with Google Cloud Identity and Access Management (IAM) for managing access controls. The specific integrations and capabilities may differ between the two tools.
Community and Ecosystem: Azure Resource Manager has a large and mature community of users and a well-established ecosystem of third-party tools and libraries. Google Cloud Deployment Manager, while gaining popularity, may have a smaller community and less extensive ecosystem. This can impact the availability of resources, tutorials, and support.

In Summary, Azure Resource Manager is focused on Azure cloud and supports JSON for defining resource templates, while Google Cloud Deployment Manager is more versatile, supporting multiple cloud providers and using YAML for configurations. The two tools also differ in deployment and update methods, supported resource types, integrations, and community support.

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Advice on Azure Resource Manager, 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

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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.

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Comments

Detailed Comparison

Azure Resource Manager	Google Cloud Deployment 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.	Google Cloud Deployment Manager allows you to specify all the resources needed for your application in a declarative format using yaml.
Deploy app resources; Organize resources; Control access to resources	-
Statistics
GitHub Stars 64	GitHub Stars -
GitHub Forks 47	GitHub Forks -
Stacks 42	Stacks 26
Followers 93	Followers 113
Votes 11	Votes 5
Pros & Cons
Pros 4 Bicep - Simple Declarative Language 2 RBAC and Policies in templates 1 Day 1 resource support 1 Deep integration with Azure services like Azure Policy 1 Over 1K samples the QuickStart repo	Pros 2 Automates infrastructure deployments 1 Fast deploy and update 1 Infrastracture as a code 1 Easy to deploy for GCP Cons 1 Only using in GCP
Integrations
Kubernetes Docker Ruby Terraform rkt	Jinja Python Google Cloud Storage Google Compute Engine Google Cloud SQL

What are some alternatives to Azure Resource Manager, Google Cloud Deployment Manager?

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.

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.

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.

Hashicorp Sentinel

Sentinel is an embeddable policy as code framework to enable fine-grained, logic-based policy decisions that can be extended to source external information to make decisions.