kube-transform 0.2.1

A lightweight open-source framework for writing and deploying distributed data transformations on Kubernetes.

kube-transform

kube-transform is a lightweight open-source framework for writing and deploying distributed, batch-oriented data pipelines on Kubernetes.

It is intentionally thin — only ~1,000 lines of code — and designed to be an easy-to-understand layer between your code and Kubernetes. Unlike heavyweight orchestration platforms like Apache Airflow (which has hundreds of thousands of lines), kube-transform aims to be simple to reason about, debug, and extend.

It focuses on simplicity and flexibility:

• Minimal required configuration
• No persistent control plane components
• Vendor-agnostic: compatible with any Kubernetes cluster, image, and file store that meet a few basic requirements

If you're looking for a quick way to get started, check out kube-transform-starter-kit for reusable setup resources like Dockerfiles, Terraform, and RBAC templates. But using the starter kit is entirely optional.

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Requirements

Your setup must meet these basic requirements:

1. Deployment Inputs

To run a pipeline, you must provide:

• pipeline_spec: A Python dictionary that conforms to the KTPipeline schema. You can optionally write your spec using the KTPipeline Pydantic model directly, and then call .model_dump() to convert it to a dict.
• image_path: A string path to your Docker image
• data_dir: A string path to your file store (must be valid from the perspective of pods running in the cluster)

2. Docker Image

• Must include Python 3.11+
• Must have kube-transform installed (e.g. via pip)
• Must include your code in /app/kt_functions/, which should be an importable module containing the functions referenced in your pipeline

3. File Store ("DATA_DIR")

• This is the directory that all pipeline jobs and the controller will read from and write to. It will be passed as the data_dir argument to run_pipeline().
• Can be a local folder (e.g. /mnt/data) or a cloud object store (e.g. s3://some-bucket/)
• Must be readable and writable by all pods in your cluster
• Compatible with anything fsspec supports

The KT controller internally uses fsspec.open() to write pipeline metadata to DATA_DIR. You must ensure that DATA_DIR is transparently accessible to all pods (including the controller). This typically means one of the following:

• You're using a mounted volume that is accessible at /mnt/data
• You've configured access via IRSA (for S3) or Workload Identity (for GCS), so the kt-pod service account has permissions to access your object store

In a single-node cluster, simply mounting a local folder to /mnt/data will work. All KT pods will have access to /mnt/data.

4. Kubernetes Cluster

• Must be able to pull your Docker image (e.g. via ECR, DockerHub, etc.)
• Must be able to access your file store
• Must include a service account named kt-pod in the default namespace, with permission to create Kubernetes Jobs
• Your deployment machine must be able to connect to the cluster (e.g. via kubectl)

For a working example setup with autoscaling, IAM roles, and RBAC configuration, see kube-transform-starter-kit.

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How to Run

Once your inputs are ready:

from kube_transform import run_pipeline

run_pipeline(pipeline_spec, image_path, data_dir)

This will:

1. Launch a temporary kt-controller Job in your Kubernetes cluster
2. Submit all pipeline jobs in the correct dependency order
3. Shut down automatically when the pipeline completes (or fails)

You can view progress using:

kubectl get pods

Make sure you have the same version of kube-transform running locally (for the run_pipeline function) as you have in your image.

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Autoscaling & Resource Management

kube-transform is compatible with both fixed-size and autoscaling clusters:

• If your cluster supports autoscaling, KT will take advantage of it automatically.
• If your cluster is fixed-size, jobs will remain in Pending state until resources are available.

For help setting up either configuration, see kube-transform-starter-kit.

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Questions? Feature requests? Open an issue on GitHub.