Fabric vs Terraform: What are the differences?
What is Fabric? Simple, Pythonic remote execution and deployment. 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..
What is Terraform? Describe your complete infrastructure as code and build resources across providers. 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.
Fabric belongs to "Server Configuration and Automation" category of the tech stack, while Terraform can be primarily classified under "Infrastructure Build Tools".
"Python" is the top reason why over 19 developers like Fabric, while over 80 developers mention "Infrastructure as code" as the leading cause for choosing Terraform.
Fabric and Terraform are both open source tools. It seems that Terraform with 17.4K GitHub stars and 4.77K forks on GitHub has more adoption than Fabric with 11.4K GitHub stars and 1.72K GitHub forks.
Uber Technologies, DigitalOcean, and 9GAG are some of the popular companies that use Terraform, whereas Fabric is used by Instagram, Coursera, and Bitbucket. Terraform has a broader approval, being mentioned in 490 company stacks & 298 developers stacks; compared to Fabric, which is listed in 147 company stacks and 38 developer stacks.
What is Fabric?
What is Terraform?
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LaunchDarkly is almost a five year old company, and our methodology for deploying was state of the art... for 2014. We recently undertook a project to modernize the way we #deploy our software, moving from Ansible-based deploy scripts that executed on our local machines, to using Spinnaker (along with Terraform and Packer) as the basis of our deployment system. We've been using Armory's enterprise Spinnaker offering to make this project a reality.
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
Our base infrastructure is composed of Debian based servers running in Amazon EC2 , asset storage with Amazon S3 , and Amazon RDS for Aurora and Redis under Amazon ElastiCache for data storage.
We are starting to work in automated provisioning and management with Terraform , Packer , and Ansible .
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
We use Fabric for automating deployment and maintenance tasks: bootstrapping and updating application servers (using the "rolling update" pattern), pulling logs from the servers, running manage.py maintenance commands.
Terraform makes it so easy to deploy AWS and Google Cloud services, with the declarative approach avoiding so many headaches of manual work and possible mistakes.
Automate everything! I have fabfiles for testing, bootstrapping, deployment, and building. Easy to customize and its pure python.
- Infrastructure as Code.
- Central tool to deploy all infratructure: AWS, CloudFlare, StatusCake
The entire AWS environments is described and setup using Terraform.