---
name: bigdata-processing-6d9e34f355d3
description: Core big data processing toolkit for data teams. Includes Polars, Dask, Vaex for large-scale data processing, ETL pipelines, and distributed computing. Use when working with datasets larger than memory, building data pipelines, or optimizing data processing performance.
---

# Big Data Processing Toolkit

## Overview

大数据团队核心处理工具集，包含高性能DataFrame库和分布式计算框架。

## Quick Reference

| 工具 | 场景 | 数据规模 |
|------|------|----------|
| **Polars** | 单机高性能分析 | GB级 |
| **Dask** | 分布式/超内存处理 | TB级 |
| **Vaex** | 超大文件惰性处理 | 100GB+ |

## 选择指南

```
数据大小判断:
├── < 10GB → Polars (最快)
├── 10GB - 100GB → Polars (streaming) 或 Dask
├── > 100GB → Dask (分布式)
└── 超大单文件 → Vaex (内存映射)

任务类型:
├── 简单ETL → Polars
├── 复杂管道 → Dask
├── 交互分析 → Vaex
└── 机器学习 → Dask + Dask-ML
```

## 子Skills

- `polars/` - 高性能DataFrame，替代Pandas
- `dask/` - 分布式计算框架
- `vaex/` - 大规模数据惰性处理
- `exploratory-data-analysis/` - 探索性数据分析
- `statistical-analysis/` - 统计分析方法
- `zarr-python/` - 分块数组存储

## 常用模式

### ETL Pipeline (Polars)
```python
import polars as pl

# 读取 -> 转换 -> 写入
(
    pl.scan_csv("raw/*.csv")
    .filter(pl.col("status") == "valid")
    .with_columns(
        pl.col("amount").cast(pl.Float64),
        pl.col("date").str.to_datetime()
    )
    .group_by("category")
    .agg(pl.col("amount").sum())
    .collect()
    .write_parquet("output/summary.parquet")
)
```

### 分布式处理 (Dask)
```python
import dask.dataframe as dd
from dask.distributed import Client

client = Client()  # 启动本地集群

ddf = dd.read_parquet("data/*.parquet")
result = ddf.groupby("key").agg({"value": "sum"}).compute()
```

### 超大文件分析 (Vaex)
```python
import vaex

df = vaex.open("huge_file.hdf5")  # 不加载到内存
df.mean(df.column)  # 惰性计算
```

## 性能最佳实践

1. **文件格式**: Parquet > CSV (10x faster)
2. **惰性计算**: 使用 `scan_*` 而非 `read_*`
3. **列选择**: 尽早选择需要的列
4. **分区策略**: 按日期/类别分区大数据集
5. **并行度**: CPU核心数 = 并行任务数

## 团队使用建议

```bash
# 查看具体skill详情
ai skills info bigdata-core/polars
ai skills info bigdata-core/dask
```

---

猪哥云-数据产品部 | 大数据团队专用

## 是什么

Big Data Processing Toolkit 用来把 数据分析师 场景里的任务输入转成可执行的流程、检查清单和交付物。

Core big data processing toolkit for data teams. Includes Polars, Dask, Vaex for large-scale data processing, ETL pipelines, and distributed computing. Use when working with datasets larger than memory, building data pipelines, or optimi...

它的价值在于让 数据AI职能线 在 Claude Code、Codex、Gemini、Hermes 或 OpenClaw 中复用同一套岗位能力，而不是依赖一次性的聊天提示词。

## 怎么用

1. 明确当前任务目标、输入材料、约束和期望交付物，再加载 `bigdata-core`。
2. 按 skill 文档中的流程、检查清单或工具建议执行，优先复用仓库已有规范与真实命令。
3. 把关键判断、风险、验证命令和产出路径记录到当前任务文档或交付说明中。
4. 用最小可证明的检查确认结果有效；发现缺口时回到 skill 清单补齐。

## 架构图

```mermaid
flowchart LR
  A[任务输入] --> B[加载 Big Data Processing Toolkit]
  B --> C[执行流程与检查清单]
  C --> D[生成交付物与风险记录]
  D --> E[验证结果并沉淀复盘]
```


---
Source attribution: MARUCIE
Original project: https://github.com/MARUCIE/openclaw-foundry/blob/f4c52cb78ad4506edbd088fde628b7a6b8468b5d/web/public/packs/data-analyst/skills/bigdata-core/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Dave AI.

## Upstream license notice

MIT License

Copyright (c) 2026 Maurice Wen

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Dave AI resource metadata

Creator / publisher: MARUCIE
Original project: https://github.com/MARUCIE/openclaw-foundry/blob/f4c52cb78ad4506edbd088fde628b7a6b8468b5d/web/public/packs/data-analyst/skills/bigdata-core/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Development & Engineering, Operations
Roles: Analytics Engineer, Business Process Analyst, Data Engineer, Data Scientist, Market Research Analyst
Specialties: Backend, Frontend
Tools: Dask, Polars, Vaex
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 0d36c3b9b591513468cbdb09a85f0b7c1c9c1ac3bb37eecb29f6ac8451c76f1c