{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/8"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/4"},{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/2"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"},{"label":"Physics","url":"https://skillfed.io/packages/category/scientific-engineering-physics"}],"enrichment":{"capability":"Dask-awkward integrates Awkward Array with Dask to enable distributed, lazy computation on complex, nested data structures across multiple cores or machines.","skillfed_tags":["distributed-computing","nested-data","scientific-computing"],"use_cases":["Process large nested datasets (e.g., physics event data) that exceed single-machine memory by distributing computation across a cluster.","Perform lazy, out-of-core analysis on ragged or columnar data without loading everything into RAM upfront.","Parallelize scientific computations on complex data structures across multiple CPU cores on a single machine.","Build data pipelines that combine Awkward Array's flexible data model with Dask's task scheduling and fault tolerance.","Integrate nested-data processing into existing Dask workflows for machine learning or data science projects."],"what_it_does":"Dask-awkward bridges two libraries: Awkward Array, which handles complex, nested, and ragged data structures, and Dask, which orchestrates distributed and lazy computation. The package lets you partition Awkward Arrays across multiple workers and compute on them in parallel, useful for scientific and data-analysis workflows where data doesn't fit in memory or where you want to exploit multiple cores. It depends on awkward, dask, cachetools, and typing-extensions.\n\nThe library is actively maintained, marked Production/Stable, and supports Python 3.10 through 3.14. Installation is straightforward via pip or conda-forge. It has no known security vulnerabilities and carries a permissive BSD-3-Clause license, making it suitable for both open-source and commercial use.","worth_installing":"Yes. Dask-awkward is actively maintained, production-ready, has no security vulnerabilities, and carries a permissive license. Install friction is low and its dependency tree is clean. It solves a real problem\u2014distributed computation on complex, nested data\u2014that has no obvious alternative in the Python ecosystem. Install it if you work with Awkward Arrays at scale or need lazy evaluation of nested structures across multiple workers."},"id":"dask-awkward","links":{"html":"https://skillfed.io/packages/dask-awkward","md":"https://skillfed.io/packages/dask-awkward.md","pypi":"https://pypi.org/project/dask-awkward/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-02-24","license_spdx":null,"license_treatment":"permissive","name":"dask-awkward","python_support":"supports_current","summary":"Awkward Array meets Dask"},"popularity":{"monthly_downloads":404206,"position":6913,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2026.2.1"}