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Puppet Labs vs Terraform: What are the differences?
# Puppet Labs vs Terraform
Puppet Labs and Terraform are two widely used configuration management and infrastructure provisioning tools in DevOps.
1. **Architecture**: Puppet Labs follows a client-server architecture where the server pushes configurations to the client nodes. In contrast, Terraform uses an Infrastructure as Code (IaC) approach with a declarative syntax to define infrastructure configurations.
2. **Programming Language**: Puppet Labs primarily uses its own declarative language (Puppet DSL) for defining configurations, whereas Terraform uses HashiCorp Configuration Language (HCL) which is more human-readable and easier to use.
3. **Resource Management**: Puppet Labs focuses on enforcing desired system configurations on existing infrastructure by managing resources, while Terraform is more focused on creating and managing cloud resources on various providers like AWS, Azure, and Google Cloud.
4. **Execution Mode**: Puppet Labs operates in an imperative mode where the order of commands matters, ensuring configurations are applied in a specific sequence. Terraform, on the other hand, functions in a declarative mode where it determines the most efficient sequence to create or modify resources based on their dependencies.
5. **Community Support**: Puppet Labs has been around longer and has a more established community with a vast collection of modules, while Terraform has gained popularity in recent years, especially in cloud-native environments, with continuous updates and a growing user base.
6. **Learning Curve**: Puppet Labs may have a steeper learning curve for beginners due to its complex domain-specific language and architecture, while Terraform's straightforward syntax and extensive documentation make it easier for newcomers to grasp and start using effectively.
In Summary, Puppet Labs and Terraform differ in their architecture, programming language, resource management approach, execution mode, community support, and learning curve, catering to different needs in the realm of configuration management and infrastructure provisioning.
I'm just getting started using Vagrant to help automate setting up local VMs to set up a Kubernetes cluster (development and experimentation only). (Yes, I do know about minikube)
I'm looking for a tool to help install software packages, setup users, etc..., on these VMs. I'm also fairly new to Ansible, Chef, and Puppet. What's a good one to start with to learn? I might decide to try all 3 at some point for my own curiosity.
The most important factors for me are simplicity, ease of use, shortest learning curve.
I have been working with Puppet and Ansible. The reason why I prefer ansible is the distribution of it. Ansible is more lightweight and therefore more popular. This leads to situations, where you can get fully packaged applications for ansible (e.g. confluent) supported by the vendor, but only incomplete packages for Puppet.
The only advantage I would see with Puppet if someone wants to use Foreman. This is still better supported with Puppet.
If you are just starting out, might as well learn Kubernetes There's a lot of tools that come with Kube that make it easier to use and most importantly: you become cloud-agnostic. We use Ansible because it's a lot simpler than Chef or Puppet and if you use Docker Compose for your deployments you can re-use them with Kubernetes later when you migrate
Ok, so first - AWS Copilot is CloudFormation under the hood, but the way it works results in you not thinking about CFN anymore. AWS found the right balance with Copilot - it's insanely simple to setup production-ready multi-account environment with many services inside, with CI/CD out of the box etc etc. It's pretty new, but even now it was enough to launch Transcripto, which uses may be a dozen of different AWS services, all bound together by Copilot.
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.
We use Terraform to manage AWS cloud environment for the project. It is pretty complex, largely static, security-focused, and constantly evolving.
Terraform provides descriptive (declarative) way of defining the target configuration, where it can work out the dependencies between configuration elements and apply differences without re-provisioning the entire cloud stack.
AdvantagesTerraform is vendor-neutral in a way that it is using a common configuration language (HCL) with plugins (providers) for multiple cloud and service providers.
Terraform keeps track of the previous state of the deployment and applies incremental changes, resulting in faster deployment times.
Terraform allows us to share reusable modules between projects. We have built an impressive library of modules internally, which makes it very easy to assemble a new project from pre-fabricated building blocks.
DisadvantagesSoftware is imperfect, and Terraform is no exception. Occasionally we hit annoying bugs that we have to work around. The interaction with any underlying APIs is encapsulated inside 3rd party Terraform providers, and any bug fixes or new features require a provider release. Some providers have very poor coverage of the underlying APIs.
Terraform is not great for managing highly dynamic parts of cloud environments. That part is better delegated to other tools or scripts.
Terraform state may go out of sync with the target environment or with the source configuration, which often results in painful reconciliation.
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.
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
Pros of Puppet Labs
- Devops52
- Automate it44
- Reusable components26
- Dynamic and idempotent server configuration21
- Great community18
- Very scalable12
- Cloud management12
- Easy to maintain10
- Free tier9
- Works with Amazon EC26
- Declarative4
- Ruby4
- Works with Azure3
- Works with OpenStack3
- Nginx2
- Ease of use1
Pros of Terraform
- Infrastructure as code121
- Declarative syntax73
- Planning45
- Simple28
- Parallelism24
- Well-documented8
- Cloud agnostic8
- It's like coding your infrastructure in simple English6
- Immutable infrastructure6
- Platform agnostic5
- Extendable4
- Automation4
- Automates infrastructure deployments4
- Portability4
- Lightweight2
- Scales to hundreds of hosts2
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Cons of Puppet Labs
- Steep learning curve3
- Customs types idempotence1
Cons of Terraform
- Doesn't have full support to GKE1
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