{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/3"},{"label":"Front-Ends","url":"https://skillfed.io/packages/category/database-front-ends"},{"label":"Other/Nonlisted Topic","url":"https://skillfed.io/packages/category/other-nonlisted-topic"}],"enrichment":{"capability":"Maggma provides a unified interface for building data processing pipelines that read from and write to diverse storage backends\u2014MongoDB, S3, local files, and others\u2014using a consistent query syntax and modular Builder components for transformation.","skillfed_tags":["etl-pipeline","data-abstraction","scientific-computing"],"use_cases":["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."],"what_it_does":"Maggma is a framework for building modular data processing pipelines in Python. It abstracts away the differences between storage backends\u2014MongoDB, Amazon S3, GridFS, local files, and others\u2014behind 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.\n\nThe 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\u2014fetching items, processing them (without I/O), and writing results\u2014so 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.","worth_installing":"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."},"id":"maggma","links":{"html":"https://skillfed.io/packages/maggma","md":"https://skillfed.io/packages/maggma.md","pypi":"https://pypi.org/project/maggma/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-22","license_spdx":null,"license_treatment":"unclear","name":"maggma","python_support":"supports_current","summary":"Framework to develop datapipelines from files on disk to full dissemenation API"},"popularity":{"monthly_downloads":659963,"position":5461,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.74.0"}