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.
- Documentation GitHub Wiki
- Bug Reports GitHub Issues
- Feature Requests GitHub Issues
- Source Code GitHub
- Discussions GitHub Discussions
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())- On-the-fly compression
- Low-memory requirements, even with terabytes of data
- Indexing and partitioning of data for fast reads
- Cursors for tracking reading position between processes
- Partial SQL DQL (Data Query Language) support
- Schema and data_expectations validation
From PyPI (recommended)
pip install --upgrade mabel
From GitHub
pip install --upgrade git+https://github.com/mabel-dev/mabel
- orjson for JSON (de)serialization
- orso for data Schemas
- zstandard for real-time on disk compression
- LZ4 for real-time in memory compression
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 ( 1) |
 |
Linux (2) |
 |
Mac (3) |
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.
