Individual skill
dask
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
backend/cli/skills/data-engineering/dask/SKILL.md
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- Nov 7, 2026
- License
- Apache-2.0
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- agent Repository topic
- ai Repository topic
- bun Repository topic
- cli Repository topic
- llm Repository topic
- ml Repository topic
- open-source Repository topic
- research Repository topic
- science Repository topic
- ai-agent Repository topic
- distributed-computing Declared skill tag
- parallel Declared skill tag
- dataframes Declared skill tag
- big-data Declared skill tag
- data Derived category
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# Dask
## Overview
Dask is a Python library for parallel and distributed computing that enables three critical capabilities:
- **Larger-than-memory execution** on single machines for data exceeding available RAM
- **Parallel processing** for improved computational speed across multiple cores
- **Distributed computation** supporting terabyte-scale datasets across multiple machines
Dask scales from laptops (processing ~100 GiB) to clusters (processing ~100 TiB) while maintaining familiar Python APIs.
## When to Use This Skill
This skill should be used when: