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data-engineer-handbook

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数据分析与数据处理 apachesparkawesomebigdatadatadataengineeringsql
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Jupyter Notebook
语言
2023-11-19
创建时间
2026-08-03
最近更新

项目能做什么

涵盖数据工程各方面学习资源的精选指南仓库。
来源:基于仓库元数据 · 内容以仓库实际 README 为准

使用说明 查看原始 README 原仓库文档

中文使用说明暂未生成。下面保留原仓库 README 摘录,命令与配置均以原文为准。
原始 README 摘录(英文原文,节选前 3000 字符)
# The Data Engineering Handbook
<a href="https://trendshift.io/repositories/8755" target="_blank"><img src="https://trendshift.io/api/badge/repositories/8755" alt="DataExpert-io%2Fdata-engineer-handbook | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>

This repo has all the resources you need to become an amazing data engineer!

### Join the free Databricks AI boot camp on August 3rd [here](https://learn.dataexpert.io/program/the-one-week-beginners-databricks-boot-camp-7129) and signup for Databricks Free Edition [here](https://signup.databricks.com/?provider=DB_FREE_TIERutm_source=github&utm_medium=video&utm_campaign=DataExpert)

## Getting started

If you are new to data engineering, start by following this [2024 breaking into data engineering roadmap](https://blog.dataengineer.io/p/the-2024-breaking-into-data-engineering)

If you are here for the [4-week free beginner boot camp](https://learn.dataexpert.io/program/the-absolute-beginner-data-engineering-boot-camp-starting-august-7th-6453/details) you can check out:
- [introduction](beginner-bootcamp/introduction.md)
- [software needed](beginner-bootcamp/software.md)

If you are here for the [6-week free intermediate boot camp](https://learn.dataexpert.io/program/free-community-boot-camp/details) you can check out
- [introduction](intermediate-bootcamp/introduction.md)
- [software needed](intermediate-bootcamp/software.md)


For more applied learning:
- Check out the [projects](projects.md) section for more hands-on examples!
- Check out the [interviews](interviews.md) section for more advice on how to pass data engineering interviews!
- Check out the [books](books.md) section for a list of high quality data engineering books
- Check out the [communities](communities.md) section for a list of high quality data engineering communities to join
- Check out the [newsletter](newsletters.md) section to learn via email 


## Resources

### Great [list of over 25 books](books.md)

Top 3 must read books are:
- [Fundamentals of Data Engineering](https://www.amazon.com/Fundamentals-Data-Engineering-Robust-Systems/dp/1098108302/)
- [Designing Data-Intensive Applications](https://www.amazon.com/Designing-Data-Intensive-Applications-Reliable-Maintainable/dp/1449373321/)
- [Designing Machine Learning Systems](https://www.amazon.com/Designing-Machine-Learning-Systems-Production-Ready/dp/1098107969)

### Great [list of over 10 communities to join](communities.md):

Top must-join communities for DE:
- [DataExpert.io Community Discord](https://discord.gg/JGumAXncAK)
- [Data Talks Club Slack](https://datatalks.club/slack)
- [Data Engineer Things Community](https://www.dataengineerthings.org/)

Top must-join communities for ML:
- [AdalFlow Discord](https://discord.com/invite/ezzszrRZvT)
- [Chip Huyen MLOps Discord](https://discord.gg/dzh728c5t3)

### Companies:

- Orchestration  
  - [Mage](https://www.mage.ai)
  - [Astronomer](https://www.astronomer.io)
  - [Prefect](https://www.prefe
阅读完整原文

项目深度分析 规则生成(AI 未配置)

综合评分 49.1 分。该项目在“个人可实施性”维度得 35 分,“付费可能性”维度得 20.0 分,差异化空间 94.0 分(评分依据见评分卡)。

面向细分需求的二次开发产品

产品构想基于 data-engineer-handbook 的核心能力,聚焦一个更窄的场景(如某平台、某行业)做差异化。
目标用户该细分场景用户
难度/周期中 · 2~6 周
收费模式买断制 / 订阅制 / 广告
竞争风险细分需求规模需验证
依据 AI 推测:基于项目主题标签的判断
以上为规则引擎基于仓库公开数据生成的参考结论,包含推测成分,不构成投资或创业承诺;配置 AI 后可获得更深入的分析。
报告会保存到 个人中心

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以上为通过 GitHub 官方 Search API 检索的相似项目,功能差异与优劣势需进一步人工确认,配置 AI 后可生成对比分析。

项目优势与风险

技术成熟度创建于 2023-11-19,最近推送 2026-08-03
社区活跃Star 45,106 / Fork 9,419 / Issue 51(数据支持,可自行核实)
许可证未检测到(需人工确认)
主要风险上游项目方向可能变化;同赛道竞争者较多(见竞品表);热度与需求不完全等价

安装与使用

此项目未提供 Docker 镜像,安装步骤以原仓库 README 为准(涉及依赖安装与环境配置,对新手门槛较高)。