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

How to Read Data

• 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.

Apache 2.0