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  1. Stackups
  2. DevOps
  3. Continuous Deployment
  4. Server Configuration And Automation
  5. GeoEngineer vs Google Cloud Deployment Manager vs Terraform

GeoEngineer vs Google Cloud Deployment Manager vs Terraform

OverviewComparisonAlternatives

Overview

Terraform
Terraform
Stacks22.9K
Followers14.7K
Votes344
GitHub Stars47.0K
Forks10.1K
Google Cloud Deployment Manager
Google Cloud Deployment Manager
Stacks24
Followers113
Votes5
GeoEngineer
GeoEngineer
Stacks52
Followers65
Votes0
GitHub Stars401
Forks54

GeoEngineer vs Google Cloud Deployment Manager vs Terraform: What are the differences?

<GeoEngineer, Google Cloud Deployment Manager, and Terraform are three popular tools used for infrastructure as code (IaC). They all serve the purpose of automating and managing infrastructure deployment in a cloud environment. Let's delve into the key differences between these three tools.>

  1. Programming Language Support: GeoEngineer primarily uses Ruby as its programming language, offering flexibility for developers who are familiar with Ruby. Google Cloud Deployment Manager uses Jinja and Python templates for configuration, while Terraform uses HashiCorp Configuration Language (HCL) which is more aligned with JSON and allows for a simpler and more readable syntax.

  2. Provider Support: Google Cloud Deployment Manager is tightly integrated with Google Cloud Platform (GCP) and is specifically designed for deploying resources on GCP. Terraform, on the other hand, supports multiple cloud providers including AWS, Azure, GCP, and more. GeoEngineer also provides support for multiple cloud providers, but its provider support may not be as extensive as Terraform.

  3. State Management: Terraform utilizes remote state storage backends which enable collaboration and state locking mechanisms to prevent conflicts when multiple users are working on the same infrastructure. GeoEngineer has similar state management capabilities but may not be as mature as Terraform in terms of features and scalability. Google Cloud Deployment Manager also has state management functionality, but it is more tightly coupled with GCP resources.

  4. Community Support: Terraform has a larger and more active community compared to GeoEngineer and Google Cloud Deployment Manager. This means that users can find more resources, documentation, and community modules to leverage in their infrastructure automation projects. GeoEngineer and Google Cloud Deployment Manager, being newer tools, may have a smaller community and fewer resources available.

In Summary, each of these tools has its strengths and weaknesses, with Terraform being a popular choice for its extensive provider support and active community, while GeoEngineer and Google Cloud Deployment Manager may be preferred for their specific integrations with certain cloud platforms.

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Detailed Comparison

Terraform
Terraform
Google Cloud Deployment Manager
Google Cloud Deployment Manager
GeoEngineer
GeoEngineer

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.

Google Cloud Deployment Manager allows you to specify all the resources needed for your application in a declarative format using yaml.

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.

Infrastructure as Code: Infrastructure is described using a high-level configuration syntax. This allows a blueprint of your datacenter to be versioned and treated as you would any other code. Additionally, infrastructure can be shared and re-used.;Execution Plans: Terraform has a "planning" step where it generates an execution plan. The execution plan shows what Terraform will do when you call apply. This lets you avoid any surprises when Terraform manipulates infrastructure.;Resource Graph: Terraform builds a graph of all your resources, and parallelizes the creation and modification of any non-dependent resources. Because of this, Terraform builds infrastructure as efficiently as possible, and operators get insight into dependencies in their infrastructure.;Change Automation: Complex changesets can be applied to your infrastructure with minimal human interaction. With the previously mentioned execution plan and resource graph, you know exactly what Terraform will change and in what order, avoiding many possible human errors
--
Statistics
GitHub Stars
47.0K
GitHub Stars
-
GitHub Stars
401
GitHub Forks
10.1K
GitHub Forks
-
GitHub Forks
54
Stacks
22.9K
Stacks
24
Stacks
52
Followers
14.7K
Followers
113
Followers
65
Votes
344
Votes
5
Votes
0
Pros & Cons
Pros
  • 121
    Infrastructure as code
  • 73
    Declarative syntax
  • 45
    Planning
  • 28
    Simple
  • 24
    Parallelism
Cons
  • 1
    Doesn't have full support to GKE
Pros
  • 2
    Automates infrastructure deployments
  • 1
    Infrastracture as a code
  • 1
    Fast deploy and update
  • 1
    Easy to deploy for GCP
Cons
  • 1
    Only using in GCP
No community feedback yet
Integrations
Heroku
Heroku
Amazon EC2
Amazon EC2
CloudFlare
CloudFlare
DNSimple
DNSimple
Microsoft Azure
Microsoft Azure
Consul
Consul
Equinix Metal
Equinix Metal
DigitalOcean
DigitalOcean
OpenStack
OpenStack
Google Compute Engine
Google Compute Engine
Jinja
Jinja
Python
Python
Google Cloud Storage
Google Cloud Storage
Google Compute Engine
Google Compute Engine
Google Cloud SQL
Google Cloud SQL
Ruby
Ruby

What are some alternatives to Terraform, Google Cloud Deployment Manager, GeoEngineer?

Ansible

Ansible

Ansible is an IT automation tool. It can configure systems, deploy software, and orchestrate more advanced IT tasks such as continuous deployments or zero downtime rolling updates. Ansible’s goals are foremost those of simplicity and maximum ease of use.

Chef

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.

Capistrano

Capistrano

Capistrano is a remote server automation tool. It supports the scripting and execution of arbitrary tasks, and includes a set of sane-default deployment workflows.

Puppet Labs

Puppet Labs

Puppet is an automated administrative engine for your Linux, Unix, and Windows systems and performs administrative tasks (such as adding users, installing packages, and updating server configurations) based on a centralized specification.

Salt

Salt

Salt is a new approach to infrastructure management. Easy enough to get running in minutes, scalable enough to manage tens of thousands of servers, and fast enough to communicate with them in seconds. Salt delivers a dynamic communication bus for infrastructures that can be used for orchestration, remote execution, configuration management and much more.

AWS CloudFormation

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.

Fabric

Fabric

Fabric is a Python (2.5-2.7) library and command-line tool for streamlining the use of SSH for application deployment or systems administration tasks. It provides a basic suite of operations for executing local or remote shell commands (normally or via sudo) and uploading/downloading files, as well as auxiliary functionality such as prompting the running user for input, or aborting execution.

AWS OpsWorks

AWS OpsWorks

Start from templates for common technologies like Ruby, Node.JS, PHP, and Java, or build your own using Chef recipes to install software packages and perform any task that you can script. AWS OpsWorks can scale your application using automatic load-based or time-based scaling and maintain the health of your application by detecting failed instances and replacing them. You have full control of deployments and automation of each component

Packer

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

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

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