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docs: move quickstart guide to a separate file under docs (#1765)

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docs: move quickstart guide to a separate doc under docs directory
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README.md
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@ -7,7 +7,7 @@
---
[Quickstart](#quickstart) |
[Quickstart](docs/quickstart.md) |
[Documentation](#documentation) |
[Features](docs/features.md) |
[Roadmap](docs/roadmap.md) |
@ -34,22 +34,7 @@ DataHub is LinkedIn's generalized metadata search & discovery tool. To learn mor
This repository contains the complete source code for both DataHub's frontend & backend. You can also read about [how we sync the changes](https://engineering.linkedin.com/blog/2020/open-sourcing-datahub--linkedins-metadata-search-and-discovery-p) between our internal fork and GitHub.
## Quickstart
1. Install [docker](https://docs.docker.com/install/) and [docker-compose](https://docs.docker.com/compose/install/) (if using Linux). Make sure to allocate enough hardware resources for Docker engine. Tested & confirmed config: 2 CPUs, 8GB RAM, 2GB Swap area.
2. Open Docker either from the command line or the desktop app and ensure it is up and running.
3. Clone this repo and `cd` into the root directory of the cloned repository.
4. Run the following command to download and run all Docker containers locally:
```
./docker/quickstart/quickstart.sh
```
This step takes a while to run the first time, and it may be difficult to tell if DataHub is fully up and running from the combined log. Please use [this guide](docs/debugging.md#how-can-i-confirm-if-all-docker-containers-are-running-as-expected-after-a-quickstart) to verify that each container is running correctly.
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, you'll notice that no data has been ingested yet.
6. To ingest provided [sample data](https://github.com/linkedin/datahub/blob/master/metadata-ingestion/mce-cli/bootstrap_mce.dat) to DataHub, switch to a new terminal window, `cd` into the cloned `datahub` repo, and run the following command:
```
./docker/ingestion/ingestion.sh
```
After running this, you should be able to see and search sample datasets in DataHub.
Please refer to the [debugging guide](docs/debugging.md) if you encounter any issues during the quickstart.
Please follow the [DataHub Quickstart Guide](docs/quickstart.md) to get a copy of DataHub up & running locally using [Docker](https://docker.com). As the guide assumes some basic knowledge of Docker, we'd recommend you to go through the "Hello World" example of [A Docker Tutorial for Beinggers](https://docker-curriculum.com) if Docker is completely foreign to you.
## Documentation
* [DataHub Developer's Guide](docs/developers.md)

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docs/quickstart.md Normal file
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# DataHub Quickstart Guide
1. Install [docker](https://docs.docker.com/install/) and [docker-compose](https://docs.docker.com/compose/install/) (if using Linux). Make sure to allocate enough hardware resources for Docker engine. Tested & confirmed config: 2 CPUs, 8GB RAM, 2GB Swap area.
2. Open Docker either from the command line or the desktop app and ensure it is up and running.
3. Clone this repo and `cd` into the root directory of the cloned repository.
4. Run the following command to download and run all Docker containers locally:
```
./docker/quickstart/quickstart.sh
```
This step takes a while to run the first time, and it may be difficult to tell if DataHub is fully up and running from the combined log. Please use [this guide](debugging.md#how-can-i-confirm-if-all-docker-containers-are-running-as-expected-after-a-quickstart) to verify that each container is running correctly.
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, you'll notice that no data has been ingested yet.
6. To ingest provided [sample data](https://github.com/linkedin/datahub/blob/master/metadata-ingestion/mce-cli/bootstrap_mce.dat) to DataHub, switch to a new terminal window, `cd` into the cloned `datahub` repo, and run the following command:
```
./docker/ingestion/ingestion.sh
```
After running this, you should be able to see and search sample datasets in DataHub.
Please refer to the [debugging guide](debugging.md) if you encounter any issues during the quickstart.