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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. To get a quick taste of Data Hub, check [Docker Quickstart Guide ](docker/quickstart ) first.
2. After you have all Docker containers running in your machine, you can ingest sample data by following
[Data Hub Ingestion Guide ](metadata-ingestion ).
3. Finally, you can start `Data Hub` by typing `http://localhost:9001` in your browser. You can sign in with `datahub`
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/ )
