2019-09-02 18:56:00 -07:00
|
|
|
# Data Hub
|
|
|
|
|
[](https://travis-ci.org/linkedin/WhereHows)
|
|
|
|
|
[](https://gitter.im/linkedin/datahub)
|
2019-09-01 16:03:45 -07:00
|
|
|
|
2019-09-08 20:25:58 -07:00
|
|
|
|
2015-11-19 14:39:21 -08:00
|
|
|
|
2019-09-08 20:25:58 -07:00
|
|
|
## Introduction
|
|
|
|
|
Data Hub is Linkedin's generalized metadata search & discovery tool. Check out the
|
|
|
|
|
[Linkedin blog post](https://engineering.linkedin.com/blog/2019/data-hub) about Data Hub. This repository is a monorepo
|
|
|
|
|
which contains complete source code to be able to build Data Hub's frontend & backend services.
|
2016-02-09 12:23:00 -08:00
|
|
|
|
2019-08-31 20:51:14 -07:00
|
|
|
## Quickstart
|
2019-09-08 20:25:58 -07:00
|
|
|
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
|
2019-09-09 01:45:49 -07:00
|
|
|
1. Add [Neo4J](http://neo4j.com) graph query support
|
|
|
|
|
2. Add user profile page
|
2019-09-08 20:25:58 -07:00
|
|
|
3. Deploy Data Hub to [Azure Cloud](https://azure.microsoft.com/en-us/)
|
