docs(quickstart): add default password to quickstart (datahub-project…

…#2875)

docs/quickstart.md

@@ -2,68 +2,67 @@
 
 ## Deploying DataHub
 
-To deploy a new instance of DataHub, perform the following steps.  
+To deploy a new instance of DataHub, perform the following steps.
 
 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. Launch the Docker Engine from command line or the desktop app. 
-
+2. Launch the Docker Engine from command line or the desktop app.
 
 3. Install the DataHub CLI
-   
+
    a. Ensure you have Python 3.6+ installed & configured. (Check using `python3 --version`)
-
-   b. Run the following commands in your terminal 
+
+   b. Run the following commands in your terminal
+
    ```
    python3 -m pip install --upgrade pip wheel setuptools
    python3 -m pip uninstall datahub acryl-datahub || true  # sanity check - ok if it fails
    python3 -m pip install --upgrade acryl-datahub
    datahub version
    ```
-   If you see "command not found", try running cli commands with the prefix 'python3 -m' instead: `python3 -m datahub version`
 
+   If you see "command not found", try running cli commands with the prefix 'python3 -m' instead: `python3 -m datahub version`
 
 4. To deploy DataHub, run the following CLI command from your terminal
+
    ```
-   datahub docker quickstart 
+   datahub docker quickstart
    ```
-   Upon completion of this step, you should be able to navigate to the DataHub UI at [http://localhost:9002](http://localhost:9002) in your browser. You can sign in using `datahub` as  username and any password (no password validation by default).
 
+   Upon completion of this step, you should be able to navigate to the DataHub UI at [http://localhost:9002](http://localhost:9002) in your browser. You can sign in using `datahub` as both the username and password.
 
 5. To ingest the sample metadata, run the following CLI command from your terminal
    ```
    datahub docker ingest-sample-data
    ```
-
-That's it! To start pushing your company's metadata into DataHub, take a look at the [Metadata Ingestion Framework](../metadata-ingestion/README.md).
 
+That's it! To start pushing your company's metadata into DataHub, take a look at the [Metadata Ingestion Framework](../metadata-ingestion/README.md).
 
 ## Resetting DataHub
 
-To cleanse DataHub of all of it's state (e.g. before ingesting your own), you can use the CLI `nuke` command. 
+To cleanse DataHub of all of it's state (e.g. before ingesting your own), you can use the CLI `nuke` command.
 
 ```
 datahub docker nuke
 ```
 
-
 ## Troubleshooting
 
 ### Command not found: datahub
 
-If running the datahub cli produces "command not found" errors inside your terminal, your system may be defaulting to an older 
+If running the datahub cli produces "command not found" errors inside your terminal, your system may be defaulting to an older
 version of Python. Try prefixing your `datahub` commands with `python3 -m`:
+
 ```
 python3 -m datahub docker quickstart
 ```
 
 ### Miscellaneous Docker issues
 
-There can be misc issues with Docker, like conflicting containers and dangling volumes, that can often be resolved by 
-pruning your Docker state with the following command. Note that this command removes all unused containers, networks, images (both dangling and unreferenced), 
+There can be misc issues with Docker, like conflicting containers and dangling volumes, that can often be resolved by
+pruning your Docker state with the following command. Note that this command removes all unused containers, networks, images (both dangling and unreferenced),
 and optionally, volumes.
 
 ```
 docker system prune
-```
+```