{"categories":[{"label":"Astronomy","url":"https://skillfed.io/packages/category/scientific-engineering-astronomy"}],"enrichment":{"capability":"Provides an abstraction layer for reading and writing astronomical data to datastores, enabling structured access to large scientific datasets through a unified interface.","skillfed_tags":["astronomy","data-access","lsst"],"use_cases":["Access and query large astronomical survey datasets from the Rubin Observatory without managing storage details directly","Build data pipelines that read and write image metadata and provenance information in a standardized way","Integrate with LSST processing systems that rely on the butler for consistent data organization","Retrieve Parquet-formatted astronomical data while managing memory efficiently in long-running analysis jobs"],"what_it_does":"lsst-daf-butler is a data access framework designed for the Vera C. Rubin Observatory's LSST project, providing a Python abstraction layer that sits between scientific applications and underlying datastores. It handles the storage and retrieval of astronomical image data and related metadata, allowing researchers to work with large datasets without needing to know the details of the underlying storage system\u2014whether files are stored locally, in cloud buckets, or in databases.\n\nThe package is built on a stack of mature dependencies: SQLAlchemy for database operations, PyArrow for efficient columnar data handling, NumPy for numerical work, and Pydantic for data validation. It requires Python 3.11 or higher and is actively maintained by the LSST collaboration. The documentation notes a known memory leak issue in PyArrow version 18 and later when reading Parquet files with the default allocator; the workaround is to set the ARROW_DEFAULT_MEMORY_POOL environment variable to jemalloc.","worth_installing":"Yes, if you are working within the LSST/Rubin Observatory ecosystem or need to integrate with their data pipelines. The package is actively maintained, has no known security vulnerabilities, and installs cleanly. However, it is a specialized tool tightly coupled to a specific observatory's data model; it is not a general-purpose data access layer. Verify that your use case aligns with LSST's data organization before adopting it."},"id":"lsst-daf-butler","links":{"html":"https://skillfed.io/packages/lsst-daf-butler","md":"https://skillfed.io/packages/lsst-daf-butler.md","pypi":"https://pypi.org/project/lsst-daf-butler/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-13","license_spdx":"BSD-3-Clause OR GPL-3.0-or-later","license_treatment":"unclear","name":"lsst-daf-butler","python_support":"supports_current","summary":"An abstraction layer for reading and writing astronomical data to datastores."},"popularity":{"monthly_downloads":176601,"position":10239,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"30.2026.3300"}