maggma

What it is and what it does

Maggma is a framework for building modular data processing pipelines in Python. It abstracts away the differences between storage backends—MongoDB, Amazon S3, GridFS, local files, and others—behind a unified Store interface that mimics PyMongo's query syntax. This lets you write pipeline code once and swap storage backends without rewriting your data access logic.

The framework's core is built around two concepts: Stores (which provide consistent read/write/query access to data) and Builders (which represent transformation steps analogous to ETL operations). Builders break work into three phases—fetching items, processing them (without I/O), and writing results—so that processing can be parallelized. Pipelines can be chained together and serialized to JSON for production deployment. It's designed for scientific data workflows and is maintained by the Materials Project team.

Use it for:

• Build a data pipeline that reads raw experimental data from S3, transforms it with a Builder, and stores results in MongoDB without rewriting query code.
• Query and aggregate data across multiple storage backends (local files, databases, cloud storage) using a single consistent interface.
• Chain multiple transformation steps into a reproducible pipeline that can be version-controlled and deployed to production as JSON.
• Parallelize data processing by separating I/O-heavy retrieval and storage from stateless transformation logic.
• Migrate data between storage systems (e.g., files to MongoDB) by swapping Store implementations while keeping pipeline logic unchanged.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Maggma provides a unified interface for building data processing pipelines that read from and write to diverse storage backends—MongoDB, S3, local files, and others—using a consistent query syntax and modular Builder components for transformation.

Yes. Maggma is actively maintained, has no known vulnerabilities, and solves a real problem for scientific and data-engineering workflows: abstracting storage backend differences. The dependency footprint is substantial but justified for a pipeline framework. Install it if you're building multi-stage data processing that needs to work across heterogeneous storage systems or if you want to parallelize transformations cleanly.

Install

maggma on PyPI

pip install maggma

uv add maggma

poetry add maggma

Installing maggma

Before you install

Low install friction with a pure-Python wheel. Active maintenance with a recent release and ongoing commits. Supports Python 3.11, 3.12, and 3.13. Twenty runtime dependencies cover database connectivity, data serialization, and async utilities—a substantial but manageable dependency footprint for a data pipeline framework.

License in practice

Licensed under a BSD 3-Clause variant with institutional copyright held by Lawrence Berkeley National Laboratory. The license permits redistribution and derivative works under standard BSD terms; no commercial restrictions.

Quickstart

pip install maggma

from maggma.stores import MongoStore

store = MongoStore(
    database="my_db",
    collection_name="my_collection",
    host="localhost",
    key="name"
)
with store:
    store.update([{"name": "item"}])
    print(store.count())

Requires a running MongoDB instance (or mongomock-ng for testing). Python 3.11 or later.

Verify before relying

• Whether the package's async capabilities (aioitertools dependency) are production-ready or experimental.
• Performance characteristics when working with very large datasets across multiple store backends.
• Whether REST API dissemination mentioned in the summary is fully implemented or a planned feature.

Package facts

Evidence: maggma-0.74.0-py3-none-any.whl

Tags

More Scientific/Engineering packages