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mabel
Advanced tools
mabel just runs when you need it, scaling to zero, making it efficient and ideal for deployments to platforms like Kubernetes, GCP Cloud Run, AWS Fargate and Knative.
We've built mabel to enable Data Analysts to write complex data engineering tasks quickly and easily, so they could get on with doing what they do best.
from mabel import Reader
data = Reader(dataset="test_data")
print(data.count())
From PyPI (recommended)
pip install --upgrade mabel
From GitHub
pip install --upgrade git+https://github.com/mabel-dev/mabel
There are a number of optional dependencies which are usually only required for specific features and functionality. These are listed in tests/requirements.txt.
mabel comes with adapters for the following data services:
| Service | |
|---|---|
| Google Cloud Storage | |
| MinIO | |
| AWS S3 | |
| Azure Blob Storage | |
| Local Storage |
Mabel is extensible with adapters for other data services as required.
mabel supports running on a range of platforms, including:
| Platform | |
|---|---|
| Docker | |
| Kubernetes | |
| Windows ( | |
| Linux ( | |
| Mac ( |
1 - Some non-core features are not available on Windows.
2 - Tested on Debian (WSL) and Ubuntu.
3 - Tested on Apple Silicon Macs.
All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.
If you have a suggestion for an improvement or a bug, raise a ticket or start a discussion.
Want to help build mabel? See the contribution guidance.
FAQs
Python Data Libraries
We found that mabel demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?

Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.

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