datahub/README.md

# DataHub: A Generalized Metadata Search & Discovery Tool
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![DataHub](docs/imgs/datahub-logo.png)

> :mega: Next DataHub town hall meeting on April 3rd, 9am-10am PDT: 
> - [Signup sheet & questions](https://docs.google.com/spreadsheets/d/1hCTFQZnhYHAPa-DeIfyye4MlwmrY7GF4hBds5pTZJYM)
> - Details and recordings of past meetings can be found [here](docs/townhalls.md)

> :sparkles:Mar 2020 Update: 
> - DataHub v0.3.1 has just been released. See [relase notes](https://github.com/linkedin/datahub/releases/tag/v0.3.1) for more details.
> - We're on [Slack](https://datahubspace.slack.com/join/shared_invite/zt-cl60ng6o-6odCh_I~ejZKE~a9GG30PA) now! Ask questions and keep up with the latest announcement.

## Introduction
DataHub is LinkedIn's generalized metadata search & discovery tool. To learn more about DataHub, check out our 
[LinkedIn blog post](https://engineering.linkedin.com/blog/2019/data-hub) and [Strata presentation](https://speakerdeck.com/shirshanka/the-evolution-of-metadata-linkedins-journey-strata-nyc-2019). 
You should also visit [DataHub Architecture](docs/architecture/architecture.md) to get a better understanding of how DataHub is implemented and [DataHub Onboarding Guide](docs/how/entity-onboarding.md) to understand how to extend DataHub for your own use case.

This repository contains the complete source code for both DataHub's frontend & backend. You can also read about [how we sync the changes](https://engineering.linkedin.com/blog/2020/open-sourcing-datahub--linkedins-metadata-search-and-discovery-p) between our the internal fork and GitHub. 

## Quickstart
1. Install [docker](https://docs.docker.com/install/) and [docker-compose](https://docs.docker.com/compose/install/) (if using Linux). Make sure to allocate enough hardware resources for Docker engine. Tested & confirmed config: 2 CPUs, 8GB RAM, 2GB Swap area.
2. Open Docker either from the command line or the desktop app and ensure it is up and running.
3. Clone this repo and `cd` into the root directory of the cloned repository.
4. Run the following command to download and run all Docker containers locally:
    ```
    cd docker/quickstart && docker-compose pull && docker-compose up --build
    ```
    This step takes a while to run the first time, and it may be difficult to tell if DataHub is fully up and running from the combined log. Please use [this guide](https://github.com/linkedin/datahub/blob/master/docs/debugging.md#how-can-i-confirm-if-all-docker-containers-are-running-as-expected-after-a-quickstart) to verify that each container is running correctly.
5. At this point, you should be able to start DataHub by opening [http://localhost:9001](http://localhost:9001) in your browser. You can sign in using `datahub` as both username and password. However, you'll notice that no data has been ingested yet.
6. To ingest provided [sample data](https://github.com/linkedin/datahub/blob/master/metadata-ingestion/mce-cli/bootstrap_mce.dat) to DataHub, switch to a new terminal window, `cd` into the cloned `datahub` repo, and run the following command:
    ```
    docker build -t ingestion -f docker/ingestion/Dockerfile . && cd docker/ingestion && docker-compose up
    ```
   After running this, you should be able to see and search sample datasets in DataHub.

Please refer to the [debugging guide](docs/debugging.md) if you encounter any issues during the quickstart.

## Documents
* [DataHub Architecture](docs/architecture/architecture.md)
* [DataHub Onboarding Guide](docs/how/entity-onboarding.md)
* [Docker Images](docker)
* [Frontend](datahub-frontend)
* [Web App](datahub-web)
* [Generalized Metadata Service](gms)
* [Metadata Ingestion](metadata-ingestion)
* [Metadata Processing Jobs](metadata-jobs)

## Releases
See [Releases](https://github.com/linkedin/datahub/releases) page for more details. We follow the [SemVer Specification](https://semver.org) when versioning the releases.

## Features & Roadmap
Check out DataHub's [Features](docs/features.md) & [Roadmap](docs/roadmap.md).

## Contributing
We welcome contributions from the community. Please refer to [the guidelines](CONTRIBUTING.md) for more details. We also have a [contrib](contrib) directory for incubation. 


## Community
Join our [slack channel](https://app.slack.com/client/TUMKD5EGJ/DV0SB2ZQV/thread/GV2TEEZ5L-1583704023.001100) for important discussions and announcements. You can also find out more about our past and upcoming [town hall meetings](https://github.com/linkedin/datahub/blob/master/docs/townhalls.md).

## FAQs
Frequently Asked Questions about DataHub can be found [here](https://github.com/linkedin/datahub/blob/master/docs/faq.md).

## Related Articles & Presentations
* [DataHub: A Generalized Metadata Search & Discovery Tool](https://engineering.linkedin.com/blog/2019/data-hub)
* [Open sourcing DataHub: LinkedIn’s metadata search and discovery platform](https://engineering.linkedin.com/blog/2020/open-sourcing-datahub--linkedins-metadata-search-and-discovery-p)
* [The evolution of metadata: LinkedIn’s story @ Strata Data Conference 2019](https://speakerdeck.com/shirshanka/the-evolution-of-metadata-linkedins-journey-strata-nyc-2019)
* [Journey of metadata at LinkedIn @ Crunch Data Conference 2019](https://www.youtube.com/watch?v=OB-O0Y6OYDE)
* [Data Catalogue — Knowing your data](https://medium.com/albert-franzi/data-catalogue-knowing-your-data-15f7d0724900)
* [How LinkedIn, Uber, Lyft, Airbnb and Netflix are Solving Data Management and Discovery for Machine Learning Solutions](https://towardsdatascience.com/how-linkedin-uber-lyft-airbnb-and-netflix-are-solving-data-management-and-discovery-for-machine-9b79ee9184bb)
* [LinkedIn元数据之旅的最新进展—Data Hub](https://zhuanlan.zhihu.com/p/80459081)
* [数据治理篇: 元数据之datahub-概述](https://www.jianshu.com/p/04630b0c63f7)
* [LinkedIn gibt die Datenplattform DataHub als Open Source frei](https://www.heise.de/developer/meldung/LinkedIn-gibt-die-Datenplattform-DataHub-als-Open-Source-frei-4663773.html)
* [Linkedin bringt Open-Source-Datahub](https://www.itmagazine.ch/artikel/71532/Linkedin_bringt_Open-Source-Datahub.html)