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DataHub Quickstart Guide

Deploying DataHub

To deploy a new instance of DataHub, perform the following steps.

  1. Install docker, jq and docker-compose v1 (if using Linux). Make sure to allocate enough hardware resources for Docker engine. Tested & confirmed config: 2 CPUs, 8GB RAM, 2GB Swap area, and 10GB disk space.

  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

    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 like python3 -m datahub version Note that DataHub CLI does not support Python 2.x.

  1. To deploy a DataHub instance locally, run the following CLI command from your terminal

    datahub docker quickstart

    This will deploy a DataHub instance using docker-compose.

    Upon completion of this step, you should be able to navigate to the DataHub UI at http://localhost:9002 in your browser. You can sign in using datahub as both the username and password.

    If you would like to modify/configure the DataHub installation in some way, please download the docker-compose.yaml used by the cli tool, modify it as necessary and deploy DataHub by passing the downloaded docker-compose file:

    datahub docker quickstart --quickstart-compose-file <path to compose file>
  2. To ingest the sample metadata, run the following CLI command from your terminal

    datahub docker ingest-sample-data

If you've enabled Metadata Service Authentication, you'll need to provide a Personal Access Token using the --token <token> parameter in the command.

That's it! Now feel free to play around with DataHub!

Next Steps

Ingest Metadata

To start pushing your company's metadata into DataHub, take a look at the Metadata Ingestion Framework.

Invite Users

To add users to your deployment to share with your team check out our Adding Users to DataHub

Enable Authentication

To enable SSO, check out Configuring OIDC Authentication or Configuring JaaS Authentication.

To enable backend Authentication, check out [authentication in DataHub's backend](/docs/introducing-metadata-service-authentication#Configuring Metadata Service Authentication).

Move to Production

We recommend deploying DataHub to production using Kubernetes. We provide helpful Helm Charts to help you quickly get up and running. Check out Deploying DataHub to Kubernetes for a step-by-step walkthrough.

Resetting DataHub

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

Updating DataHub locally

If you have been testing DataHub locally, a new version of DataHub got released and you want to try the new version then you can use below commands.

datahub docker nuke --keep-data
datahub docker quickstart

This will keep the data that you have ingested so far in DataHub and start a new quickstart with the latest version of DataHub.


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 version of Python. Try prefixing your datahub commands with python3 -m:

python3 -m datahub docker quickstart

Another possibility is that your system PATH does not include pip's $HOME/.local/bin directory. On linux, you can add this to your ~/.bashrc:

if [ -d "$HOME/.local/bin" ] ; then

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), and optionally, volumes.

docker system prune