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# Data Hub
[](https://travis-ci.org/linkedin/WhereHows)
[](https://gitter.im/linkedin/datahub)
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## Introduction
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Data Hub is Linkedin's generalized metadata search & discovery tool. To learn more about Data Hub, 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 ). This repository contains the complete source code to be able to build Data Hub's frontend & backend services.
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## Quickstart
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1. Install [docker ](https://docs.docker.com/install/ ) and [docker-compose ](https://docs.docker.com/compose/install/ ).
2. Clone this repo and make sure you are at the `datahub` branch.
3. Run below command to download and run all Docker containers in your local:
```
cd docker/quickstart & & docker-compose pull & & docker-compose up --build
```
4. After you have all Docker containers running in your machine, run below command to ingest provided sample data to Data Hub:
```
./gradlew :metadata-events:mxe-schemas:build & & cd metadata-ingestion/mce-cli & & python mce_cli.py produce -d bootstrap_mce.dat
```
5. Finally, you can start `Data Hub` by typing `http://localhost:9001` in your browser. You can sign in with `datahub`
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as username and password.
## Quicklinks
* [Docker Images ](docker )
* [Frontend App ](datahub-frontend )
* [Generalized Metadata Store ](gms )
* [Metadata Consumer Jobs ](metadata-jobs )
* [Metadata Ingestion ](metadata-ingestion )
## Roadmap
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1. Add [Neo4J ](http://neo4j.com ) graph query support
2. Add user profile page
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3. Deploy Data Hub to [Azure Cloud ](https://azure.microsoft.com/en-us/ )
