Tools don't like to fight, but people love to set them up for "X-tool vs. Y-tool" grudge matches, thinking that's how you cover them in pride and glory. This blog sheds light on two of the heavy-weight contenders in the DevOps world and makes an attempt to clarify some common misconceptions. If you understand databases and data engineering but struggle to understand when to use Kubernetes and when to use Terraform for your data infrastructure, read along.
Both Kubernetes and Terraform are open-source projects in the DevOps space. While Kubernetes is an orchestration tool for managing containers, Terraform lets you define your infrastructure as code, whether your applications run as containers or a giant monolith. A minimum entry fee for using Kubernetes is using containers. But when you run your applications as containers, who sets up the underlying infrastructure for you (compute, storage, and network)? Your applications do not run on thin air. In the pre-Terraform-era, a SysAdmin would write scripts or Ansible playbooks to stand up and configure virtual machines and/or bare-metal servers. With Terraform, you can declare how you plan the application/data infrastructure, and Terraform executes based on your plan.
You can use both these tools at once: Terraform to create/manage your infrastructure, Kubernetes to manage the containers that house your applications.
From an application perspective, tools like Kubernetes and Terraform complement each other. Both tools allow you to declaratively define your system, and they execute to match the running system to what's defined in code. They also offer modularity and reusability for your system as a whole or parts of it. This way, you can copy the exact same infrastructure multiple times while only writing it once.
From a data perspective, Terraform can also help provision and manage stateful infrastructure. Whether it's a database or a streaming platform, you can specify the exact configuration and Terraform takes care of the underlying dependency and networking to stand up your data infrastructure.
Kubernetes operators make it easier to define custom resources like databases and streaming platforms in Kubernetes. By using Kubernetes's built-in scheduling feature, you can also use these operators to create and manage your data infrastructure in a declarative way.
Now that you understand the power of Kubernetes and Terraform, let's have a look at how you can use them to provision your data infrastructure on Aiven. Both of them have Aiven integrations: the Aiven Terraform Provider and the Aiven Operator for Kubernetes offer ways to create your data resources programmatically. With Aiven offering an intuitive web console, powerful CLI, and these two DevOps tools, how do you choose which tool to use for your business?
If you're already familiar with Terraform and use it to provision other infrastructure resources, the Aiven Terraform Provider can be a perfect choice. You can use your existing skillset to create and manage Aiven resources. The Terraform provider is also good for doing dry-runs of your infrastructure provisioning to preview the resources that it will create. Terraform also takes care of the underlying dependencies of the resources you define. This means that Terraform will know which Aiven resource to create first, as other resources might be dependent upon it.
The Aiven Operator for Kubernetes, on the other hand, makes sense if you already have your applications running on Kubernetes and need a single control plane to manage both application and data resources. Currently, this operator only supports Aiven for PostgreSQL® and Aiven for Apache Kafka®. If you choose this tool, you can benefit from Kubernetes's self-healing and auto-scaling properties.
As two of the most popular open-source projects, both Kubernetes and Terraform have their own use cases for deploying, monitoring, upgrading, backing up, and restoring services. Check out some more articles to learn more about these tools and how you can automate the provisioning of your data infrastructure:
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