Puppet Labs vs Terraform: What are the differences?
Puppet Labs: Server automation framework and application. 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; 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.
Puppet Labs belongs to "Server Configuration and Automation" category of the tech stack, while Terraform can be primarily classified under "Infrastructure Build Tools".
Some of the features offered by Puppet Labs are:
- Insight- Puppet Enterprise's event inspector gives immediate and actionable insight into your environment, showing you what changed, where and how by classes, nodes and resources.
- Discovery- Puppet Enterprise delivers a dynamic and fully-pluggable discovery service that allows you to take advantage of any data source or real-time query results to quickly locate, identify and group cloud nodes.
- Provisioning- Automatically provision and configure bare metal, virtual, and private or public cloud capacity, all from a single pane. Save time getting your cloud projects off the ground by reusing the same configuration modules you set up for your physical deployments.
On the other hand, Terraform provides the following key features:
- 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.
"Devops" is the primary reason why developers consider Puppet Labs over the competitors, whereas "Infrastructure as code" was stated as the key factor in picking Terraform.
Puppet Labs and Terraform are both open source tools. It seems that Terraform with 17.7K GitHub stars and 4.83K forks on GitHub has more adoption than Puppet Labs with 5.37K GitHub stars and 2.1K GitHub forks.
According to the StackShare community, Terraform has a broader approval, being mentioned in 509 company stacks & 312 developers stacks; compared to Puppet Labs, which is listed in 180 company stacks and 49 developer stacks.
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By 2014, the DevOps team at Lyft decided to port their infrastructure code from Puppet to Salt. At that point, the Puppet code based included around "10,000 lines of spaghetti-code,” which was unfamiliar and challenging to the relatively new members of the DevOps team.
“The DevOps team felt that the Puppet infrastructure was too difficult to pick up quickly and would be impossible to introduce to [their] developers as the tool they’d use to manage their own services.”
To determine a path forward, the team assessed both Ansible and Salt, exploring four key areas: simplicity/ease of use, maturity, performance, and community.
They found that “Salt’s execution and state module support is more mature than Ansible’s, overall,” and that “Salt was faster than Ansible for state/playbook runs.” And while both have high levels of community support, Salt exceeded expectations in terms of friendless and responsiveness to opened issues.
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.
Since #ATComputing is a vendor independent Linux and open source specialist, we do not have a favorite Linux distribution. We mainly use Ubuntu , Centos Debian , Red Hat Enterprise Linux and Fedora during our daily work. These are also the distributions we see most often used in our customers environments.
For our #ci/cd training, we use an open source pipeline that is build around Visual Studio Code , Jenkins , VirtualBox , GitHub , Docker Kubernetes and Google Compute Engine.
For #ServerConfigurationAndAutomation, we have embraced and contributed to Ansible mainly because it is not only flexible and powerful, but also straightforward and easier to learn than some other (open source) solutions. On the other hand: we are not affraid of Puppet Labs and Chef either.
Currently, our most popular #programming #Language course is Python . The reason Python is so popular has to do with it's versatility, but also with its low complexity. This helps sysadmins to write scripts or simple programs to make their job less repetitive and automating things more fun. Python is also widely used to communicate with (REST) API's and for data analysis.
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 .
I'm using puppet to configure my servers. This makes it really simple to ensure that I have the same environment. There is a bit of a learning curve, but the repeatability definitely makes it worth the effort. I found puppet to be a little easier to pick up relative to chef, but I've used both. They're both great solutions.
I really like that there are a lot of modules available on the puppet forge that are being actively maintained.
We provision all servers with puppet. We have one central Puppet server which uses puppet modules referenced by a Puppetfile. Those puppet modules are partly from forge and partly self written.
All modules which are self written, have to be tested using rspec-puppet and beaker.
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.
Opstax uses puppet for role/profile based configuration management and the distribution of small/static code.
- Infrastructure as Code.
- Central tool to deploy all infratructure: AWS, CloudFlare, StatusCake
Configures or servers and allows us to be region independent we have 5 regions across the globe.
The entire AWS environments is described and setup using Terraform.