Comparison
awesome-pipeline vs ploomber
Verdict
Pick awesome-pipeline if a curated list of pipeline toolkits for diverse applications; pick ploomber if ploomber is a Python-based tool that specializes in iterative development and deployment of data pipelines, supporting Jupyter notebooks and integrating smoothly with popular IDEs like PyCharm and VSCode.
Markdown twin · awesome-pipeline alternatives · ploomber alternatives
GraphCanon updated Sep 29, 2026
8views this month
vs
Trust & integrity
| Signal | awesome-pipeline | ploomber |
|---|---|---|
| Maintenance | Very active (6d since push) As of Sep 15, 2026 · github_public_v1 | Archived (487d since push) As of Sep 29, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 15, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 29, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 15, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- awesome-pipeline
- Curated list of pipeline toolkits
- ploomber
- The fastest way to build data pipelines. Develop iteratively, deploy anywhere.
Stars
- awesome-pipeline
- 6.6k
- ploomber
- 3.6k
Forks
- awesome-pipeline
- 650
- ploomber
- 243
Open issues
- awesome-pipeline
- 33
- ploomber
- 110
Language
- awesome-pipeline
- -
- ploomber
- Python
Adopt for
- awesome-pipeline
- A curated list of pipeline toolkits for diverse applications.
- ploomber
- Ploomber is a Python-based tool that specializes in iterative development and deployment of data pipelines, supporting Jupyter notebooks and integrating smoothly with popular IDEs like PyCharm and VSCode.
Persona
- awesome-pipeline
- -
- ploomber
- -
Runtime
- awesome-pipeline
- -
- ploomber
- -
License
- awesome-pipeline
- -
- ploomber
- Ploomber operates under the Apache License 2.0 which allows free use, modification and distribution, provided that any redistributed code includes an acknowledgement of the original license.
Last pushed
- awesome-pipeline
- Sep 8, 2026
- ploomber
- May 29, 2025
Categories
- awesome-pipeline
- Developer Tools
- ploomber
- Developer Tools
Trust and health
Maintenance
- awesome-pipeline
- Very active (96%)
- ploomber
- Archived (8%)
Days since push
- awesome-pipeline
- 6d
- ploomber
- 487d
Archived on GitHub
- awesome-pipeline
- No
- ploomber
- Yes
Open issues (now)
- awesome-pipeline
- 33
- ploomber
- 110
Stars delta
- awesome-pipeline
- +8 (30d)
- ploomber
- -2 (30d)
Open issues delta
- awesome-pipeline
- -1 (30d)
- ploomber
- 0 (30d)
Owner type
- awesome-pipeline
- User
- ploomber
- Organization
Full report
- awesome-pipeline
- Trust report
- ploomber
- Trust report
Shared compatibility
- Python · awesome-pipeline: Python runtime · ploomber: Python runtime
Choose awesome-pipeline if…
- Tags unique to awesome-pipeline: automation, pipeline, workflow.
- You need a comprehensive overview of various workflow and pipeline management tools
- More GitHub stars (6.6k vs 3.6k) - visibility, not fit.
When NOT to use awesome-pipeline
- Seeking specific functionality rather than an aggregated list of options
- Looking for direct implementation guidance without further research into individual tools
Choose ploomber if…
- Requirements: Works with Python versions 3.7 and higher..
- Tags unique to ploomber: data-engineering, data-science, jupyter-notebooks, machine-learning.
- Use Ploomber when you need to iteratively develop and test data pipelines using Python, as it provides native support for such workflows.
When NOT to use ploomber
- Avoid Ploomber if your development process does not involve iterative testing or if direct integration with Jupyter notebooks is unnecessary.
- Do not use Ploomber if you prefer a non-IDE environment and you do not need Python's ecosystem for building data pipelines, as it heavily integrates with IDEs like PyCharm and VSCode.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (pditommaso/awesome-pipeline) · observed Sep 20, 2026
- GitHub forks (pditommaso/awesome-pipeline) · observed Sep 20, 2026
- Last push (pditommaso/awesome-pipeline) · observed Sep 8, 2026
- License file (unknown) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- GitHub stars (ploomber/ploomber) · observed Sep 29, 2026
- GitHub forks (ploomber/ploomber) · observed Sep 29, 2026
- Last push (ploomber/ploomber) · observed May 29, 2025
- License file (Apache-2.0) · observed Sep 29, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-pipeline 6.6k · ploomber 3.6k (synced Sep 20, 2026).
Common questions
- What is the difference between awesome-pipeline and ploomber?
- awesome-pipeline: Curated list of pipeline toolkits. ploomber: The fastest way to build data pipelines. Develop iteratively, deploy anywhere.. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-pipeline over ploomber?
- Choose awesome-pipeline over ploomber when Tags unique to awesome-pipeline: automation, pipeline, workflow; You need a comprehensive overview of various workflow and pipeline management tools; More GitHub stars (6.6k vs 3.6k) - visibility, not fit.
- When should I choose ploomber over awesome-pipeline?
- Choose ploomber over awesome-pipeline when Requirements: Works with Python versions 3.7 and higher.; Tags unique to ploomber: data-engineering, data-science, jupyter-notebooks, machine-learning; Use Ploomber when you need to iteratively develop and test data pipelines using Python, as it provides native support for such workflows.
- When should I avoid awesome-pipeline?
- Seeking specific functionality rather than an aggregated list of options Looking for direct implementation guidance without further research into individual tools
- When should I avoid ploomber?
- Avoid Ploomber if your development process does not involve iterative testing or if direct integration with Jupyter notebooks is unnecessary. Do not use Ploomber if you prefer a non-IDE environment and you do not need Python's ecosystem for building data pipelines, as it heavily integrates with IDEs like PyCharm and VSCode.
- Is awesome-pipeline or ploomber more popular on GitHub?
- awesome-pipeline has more GitHub stars (6,624 vs 3,620). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-pipeline and ploomber open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to awesome-pipeline or ploomber?
- GraphCanon lists graph-backed alternatives at awesome-pipeline alternatives and ploomber alternatives (awesome-pipeline markdown twin, ploomber markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, awesome-pipeline or ploomber?
- awesome-pipeline: Very active. ploomber: Archived. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for awesome-pipeline and ploomber?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-pipeline trust report; ploomber trust report.