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29 lines
1.3 KiB
Markdown
29 lines
1.3 KiB
Markdown
# Data Hub
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[](https://travis-ci.org/linkedin/WhereHows)
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[](https://gitter.im/linkedin/datahub)
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## Introduction
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Data Hub is Linkedin's generalized metadata search & discovery tool. Check out the
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[Linkedin blog post](https://engineering.linkedin.com/blog/2019/data-hub) about Data Hub. This repository is a monorepo
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which contains 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.
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2. After you have all Docker containers running in your machine, you can ingest sample data by following
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[Data Hub Ingestion Guide](metadata-ingestion).
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3. 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.
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## Quicklinks
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* [Docker Images](docker)
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* [Frontend App](datahub-frontend)
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* [Generalized Metadata Store](gms)
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* [Metadata Consumer Jobs](metadata-jobs)
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* [Metadata Ingestion](metadata-ingestion)
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## Roadmap
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1. Add [Neo4J](http://neo4j.com) graph query support
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2. Add user profile page
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3. Deploy Data Hub to [Azure Cloud](https://azure.microsoft.com/en-us/) |