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 to be able to build DataHub's frontend & backend services.
This step takes long time and it might be hard to figure out when DataHub is fully up. You can refer to [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 if DataHub is up and running.
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, there is no data just yet.
6. To ingest [provided](https://github.com/linkedin/datahub/blob/master/metadata-ingestion/mce-cli/bootstrap_mce.dat) sample data to DataHub, switch to a new terminal, `cd` into the cloned `datahub` repo, and run below command:
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.
