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https://github.com/data-engineering-community/data-engineering-wiki

The best place to learn data engineering. Built and maintained by the data engineering community.
https://github.com/data-engineering-community/data-engineering-wiki

data data-engineer data-engineering data-modeling data-pipelines database etl sql

Last synced: 2 days ago
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The best place to learn data engineering. Built and maintained by the data engineering community.

Host: GitHub
URL: https://github.com/data-engineering-community/data-engineering-wiki
Owner: data-engineering-community
License: cc0-1.0
Created: 2021-05-04T00:07:33.000Z (over 3 years ago)
Default Branch: main
Last Pushed: 2024-10-27T20:09:59.000Z (3 months ago)
Last Synced: 2024-10-29T19:59:37.790Z (2 months ago)
Topics: data, data-engineer, data-engineering, data-modeling, data-pipelines, database, etl, sql
Language: CSS
Homepage: https://dataengineering.wiki
Size: 7.67 MB
Stars: 1,396
Watchers: 28
Forks: 153
Open Issues: 6
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

jimsghstars - data-engineering-community/data-engineering-wiki - The best place to learn data engineering. Built and maintained by the data engineering community. (CSS)

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# Data Engineering Wiki

The best place to learn data engineering. Built and maintained by the [data engineering community](https://dataengineering.wiki/Community/Community).

[![Subreddit subscribers](https://img.shields.io/reddit/subreddit-subscribers/dataengineering?style=social)](https://www.reddit.com/r/dataengineering/)



## What's inside?

A collection of notes that are connected organically but loosely organized into the following categories:

1. **[Concepts](https://dataengineering.wiki/Concepts/Concepts):** Concepts related to Data Engineering.

2. **[FAQ](https://dataengineering.wiki/FAQ/FAQ):** Frequently asked questions about Data Engineering.

3. **[Guides](https://dataengineering.wiki/Guides/Guides):** Understand how to make Data Engineering decisions.

4. **[Tools](https://dataengineering.wiki/Tools/Tools):** Commonly used tools for Data Engineering.

5. **[Tutorials](https://dataengineering.wiki/Tutorials/Tutorials):** Step-by-step instructions for Data Engineering tasks.

6. **[Learning Resources](https://dataengineering.wiki/Learning+Resources):** Learn Data Engineering with resources recommended by the Data Engineering community.

## Sponsors

The Data Engineering Wiki is an CC0-1.0-licensed open source project with its ongoing development made possible entirely by the support of these awesome [backers](https://github.com/data-engineering-community/data-engineering-wiki/blob/main/BACKERS.md). If you'd like to join them, please consider [sponsoring the Data Engineering Wiki's development](https://github.com/sponsors/data-engineering-community).



## Gold Sponsors



    





    



## Silver Sponsors



    





## How to run it locally

The wiki can be used offline and can be used as-is or incorporated into your own personal knowledge management system. It is built to be used with [Obsidian](https://obsidian.md/) (free, no affiliation) but is compatible with other tools as well such as [Foam](https://github.com/foambubble/foam) or [Roam Research](https://roamresearch.com/).

1. Download this GitHub repository.

2. Download the free [Obsidian desktop app](https://obsidian.md/).

3. Run the Obsidian app and choose **Open folder as vault**, click **Open**.

4. In the file browser, choose the folder where you downloaded the GitHub repository, click **Open**.

See [Obsidian help](https://help.obsidian.md/) for questions on using Obsidian.

## Contributing

There are many different ways to contribute to the wiki's development. If you're interested, check out our [contributing guidelines](https://github.com/data-engineering-community/data-engineering-wiki/blob/main/CONTRIBUTING.md) to learn how you can get involved.

Thank you to all of our [contributors](https://github.com/data-engineering-community/data-engineering-wiki/graphs/contributors) who shared their data engineering knowledge!