# ploomber/ploomber

The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️

Repository: https://github.com/ploomber/ploomber
Canonical: https://ross.abutalabs.com/products/ploomber
Homepage: https://docs.ploomber.io
Language: Python
License: Apache-2.0
License Family: permissive
Topics: workflow, machine-learning, data-science, data-engineering, mlops, papermill, jupyter, jupyter-notebooks, pipelines, vscode, pycharm, notebooks
Archived: true
Last push: 2025-05-29T22:02:03+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 24, release rhythm 35, longevity 100
- inputs: {"age_days": 2417, "days_push": 461, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3622, forks 244 (observed 2026-08-28T04:08:11.884235+00:00)

## What it is
Ploomber is a Python framework for building maintainable data pipelines from scripts and Jupyter notebooks, with iterative local development and deployment to Kubernetes, Airflow, AWS Batch, or SLURM without code changes. It can also refactor legacy notebooks into modular pipelines with a single command.

## Use cases
- build data pipelines from jupyter notebooks
- convert notebooks into modular pipelines
- deploy data pipelines to airflow or kubernetes
- orchestrate machine learning workflows in python
- develop data pipelines iteratively in vscode or pycharm
- run pipelines on aws batch or slurm

## When to choose
- your pipelines are built from Python scripts or Jupyter notebooks
- you want interactive, iterative development with production deployment
- you need to deploy the same pipeline code to multiple backends like Airflow, Kubernetes, or SLURM
- you have legacy notebooks that need refactoring into modular pipelines

## When to avoid
- you need a general-purpose workflow scheduler for non-Python tasks
- your team is standardized on another orchestrator like Dagster or Prefect
- you only need simple one-off scripts with no pipeline structure

## Facets
- artifact type: framework
- maturity: active
- function: workflow-automation, etl, machine-learning, data-science, scheduling
- domain: data-science, machine-learning, developer-tools
- platform: python, cross-platform, cloud
- tags: data-pipelines, jupyter-notebooks, mlops, airflow, papermill, notebook-pipelines, data-engineering, kubernetes

## Member repositories
- ploomber/ploomber (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:11.884235+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:31:57.695547+00:00, confidence not recorded.
  - readme: https://github.com/ploomber/ploomber (fetched 2026-08-28T04:08:11.884235+00:00, sha 8c8e3e8b90ba)
  - registry_pypi: https://pypi.org/pypi/ploomber/json (fetched 2026-08-29T09:26:41.973324+00:00, sha aef627396951)
- Data as of 2026-08-30T08:39:29.467469+00:00.