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  1. Stackups
  2. DevOps
  3. Continuous Deployment
  4. Server Configuration And Automation
  5. AWS CodePipeline vs Terraform

AWS CodePipeline vs Terraform

OverviewDecisionsComparisonAlternatives

Overview

Terraform
Terraform
Stacks22.9K
Followers14.7K
Votes344
GitHub Stars47.0K
Forks10.1K
AWS CodePipeline
AWS CodePipeline
Stacks551
Followers933
Votes30

AWS CodePipeline vs Terraform: What are the differences?

Introduction

AWS CodePipeline and Terraform are two popular tools used in the DevOps process. While both of them are used for infrastructure management and automation, they have some key differences that set them apart.

  1. Deployment Pipeline vs Infrastructure as Code: AWS CodePipeline is primarily a deployment orchestration tool that helps in automating the release process. It allows you to create a pipeline with a series of stages, and each stage can have multiple actions that execute different tasks. On the other hand, Terraform is an infrastructure as code tool that helps in creating, managing, and versioning the infrastructure resources in a declarative manner.

  2. Simplicity vs Flexibility: CodePipeline is a managed service provided by AWS, which means it takes care of all the underlying infrastructure and resources. This makes it easy to set up and use, especially for beginners or those who prefer a simpler approach. Terraform, on the other hand, provides more flexibility and control as it allows you to define infrastructure resources using its own declarative language called HCL (HashiCorp Configuration Language). With Terraform, you can define complex infrastructure setups and manage them effectively.

  3. Native vs Multi-Cloud Support: CodePipeline is a native AWS service, which means it is tightly integrated with other AWS services like AWS CodeCommit, AWS CodeBuild, AWS CodeDeploy, etc. This makes it easier to set up a pipeline that leverages these services. On the other hand, Terraform is a cloud-agnostic tool that supports multiple cloud providers like AWS, Azure, Google Cloud, etc. It allows you to manage resources across different cloud platforms and create a consistent infrastructure provisioning process.

  4. Continuous Integration vs Infrastructure Provisioning: CodePipeline focuses more on the continuous integration and deployment aspects of the DevOps process. It can be used to integrate with various code repositories, build and test frameworks, and deployment mechanisms. On the other hand, Terraform focuses more on the infrastructure provisioning part. It provides a way to define infrastructure resources in code and then apply those changes to create or update the infrastructure.

  5. Managed Service vs Standalone Tool: CodePipeline is a fully managed service provided by AWS, which means AWS takes care of all the underlying infrastructure, scaling, and maintenance. This makes it easier for teams to set up and use without worrying about the operational overhead. Terraform, on the other hand, is a standalone tool that needs to be installed and managed separately. While this provides more control, it also requires additional effort for maintenance and updates.

  6. Cost Structure: CodePipeline follows a pay-as-you-go model, where you pay for the number of pipeline executions and associated resources used. The cost depends on factors like the number of stages, actions, and the type of resources used. Terraform, being an open-source tool, is free to use. However, you still need to pay for the cloud resources that Terraform provisions and manages.

In summary, AWS CodePipeline is a managed service that focuses on deployment orchestration and continuous integration, while Terraform is a standalone tool that provides more flexibility and control for infrastructure provisioning through declarative code. CodePipeline is tightly integrated with AWS services and provides simplicity, while Terraform is cloud-agnostic, supports multiple cloud platforms, and offers more advanced infrastructure management capabilities.

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Advice on Terraform, AWS CodePipeline

Sung Won
Sung Won

Nov 4, 2019

DecidedonGoogle Cloud IoT CoreGoogle Cloud IoT CoreTerraformTerraformPythonPython

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
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
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

Terraform
Terraform
AWS CodePipeline
AWS CodePipeline

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.

CodePipeline builds, tests, and deploys your code every time there is a code change, based on the release process models you define.

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
Workflow Modeling;AWS Integrations;Pre-Built Plugins;Custom Plugins;Declarative Templates;Access Control
Statistics
GitHub Stars
47.0K
GitHub Stars
-
GitHub Forks
10.1K
GitHub Forks
-
Stacks
22.9K
Stacks
551
Followers
14.7K
Followers
933
Votes
344
Votes
30
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
  • 13
    Simple to set up
  • 8
    Managed service
  • 4
    GitHub integration
  • 3
    Parallel Execution
  • 2
    Automatic deployment
Cons
  • 2
    No project boards
  • 1
    No integration with "Power" 365 tools
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
Runscope
Runscope
Amazon S3
Amazon S3
GitHub
GitHub
Jenkins
Jenkins
CloudBees
CloudBees
BlazeMeter
BlazeMeter
Ghost Inspector
Ghost Inspector
AWS Elastic Beanstalk
AWS Elastic Beanstalk
Amazon EC2
Amazon EC2

What are some alternatives to Terraform, AWS CodePipeline?

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.

Buddy

Buddy

Git platform for web and software developers with Docker-based tools for Continuous Integration and Deployment.

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.

Cloud 66

Cloud 66

Cloud 66 gives you everything you need to build, deploy and maintain your applications on any cloud, without the headache of dealing with "server stuff". Frameworks: Ruby on Rails, Node.js, Jamstack, Laravel, GoLang, and more.

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.

DeployBot

DeployBot

DeployBot makes it simple to deploy your work anywhere. You can compile or process your code in a Docker container on our infrastructure, and we'll copy it to your servers once everything has been successfully built.

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

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