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Version: Next

Configuring Your Looker & LookerML Connector to DataHub

Now that you have created a DataHub specific API key with the relevant access in the prior step, it's now time to set up a connection via the DataHub UI.

Configure LookerML Ingestion​

Configure Secrets​

  1. Within DataHub, navigate to the Ingestion tab in the top, right corner of your screen

Navigate to the "Ingestion Tab"

note

If you do not see the Ingestion tab, please contact your DataHub admin to grant you the correct permissions

  1. Navigate to the Secrets tab and click Create new secret.

Secrets Tab

  1. Create a client id secret

    This will securely store your Looker API Key Client ID within DataHub

    • Enter a name like LOOKER_CLIENT_ID - we will use this later to refer to the API Key Client ID
    • Enter the API Key Client ID
    • Optionally add a description
    • Click Create

API Key Client ID

  1. Create a secret to store the API Key Client Secret

    This will securely store your client secret"

    • Enter a name like LOOKER_CLIENT_SECRET - we will use this later to refer to the client secret
    • Enter the client secret
    • Optionally add a description
    • Click Create

API Key client secret

Configure Recipe​

  1. Navigate to the Sources tab and click Create new source

    Click "Create new source"

  2. Choose Looker

    Select Looker from the options

  3. Enter details into the Looker Recipe

    You need to set minimum 3 field in the recipe:

    a. Base URL: This is your looker instance URL. For example https://abc.cloud.looker.com.

    b. Client ID: Use the secret LOOKER_CLIENT_ID with the format ${LOOKER_CLIENT_ID}.

    c. Client Secret: Use the secret LOOKER_CLIENT_SECRET with the format ${LOOKER_CLIENT_SECRET}.

Optionally, use the dashboard_pattern and chart_pattern fields to filter for specific dashboard and chart.

config:
     ...
     dashboard_pattern:
        allow:
          - "2"
     chart_pattern:
        allow:
          - "258829b1-82b1-4bdb-b9fb-6722c718bbd3"

Your recipe should look something like this:

Looker Recipe

After completing the recipe, click Next.

Schedule Execution​

Now it's time to schedule a recurring ingestion pipeline to regularly extract metadata from your Looker instance.

  1. Decide how regularly you want this ingestion to run-- day, month, year, hour, minute, etc. Select from the dropdown

schedule selector

  1. Ensure you've configured your correct timezone

    timezone_selector

  2. Click Next when you are done

Finish Up​

Name your ingestion source, then click Save and Run

Name your ingestion

You will now find your new ingestion source running

ingestion_running

Validate Ingestion Runs​

  1. View the latest status of ingestion runs on the Ingestion page

ingestion succeeded

  1. Click the plus sign to expand the full list of historical runs and outcomes; click Details to see the outcomes of a specific run

ingestion_details

  1. From the Ingestion Run Details page, pick View All to see which entities were ingested

ingestion_details_view_all

  1. Pick an entity from the list to manually validate if it contains the detail you expected

ingestion_details_view_all

Configuring LookML Connector​

Now that you have created a DataHub specific API key and Deploy Key with the relevant access in the prior step, it's now time to set up a connection via the DataHub UI.

Configure Recipe​

  1. Navigate to the Sources tab and click Create new source

    Click "Create new source"

  2. Choose LooML

    Select Looker from the options

  3. Enter details into the Looker Recipe

    You need to set minimum 5 field in the recipe for this quick ingestion guide:

    a. GitHub Repo: This is your github repository where LookML model are stored. You can provide full URL (example: https://gitlab.com/gitlab-org/gitlab) or organization/repo in this case connector assume it is GitHub repo

    b. GitHub Deploy Key: Copy the content of looker_datahub_deploy_key and paste into this filed.

    c. Looker Base URL: This is your looker instance URL. For example https://abc.cloud.looker.com

    d. Looker Client ID: Use the secret LOOKER_CLIENT_ID with the format ${LOOKER_CLIENT_ID}.

    e. Looker Client Secret: Use the secret LOOKER_CLIENT_SECRET with the format ${LOOKER_CLIENT_SECRET}.

Your recipe should look something like this:

LookML Recipe

After completing the recipe, click Next.

Schedule Execution​

Now it's time to schedule a recurring ingestion pipeline to regularly extract metadata from your Looker instance.

  1. Decide how regularly you want this ingestion to run-- day, month, year, hour, minute, etc. Select from the dropdown

schedule selector

  1. Ensure you've configured your correct timezone

    timezone_selector

  2. Click Next when you are done

Finish Up​

  1. Name your ingestion source, then click Save and Run

    Name your ingestion

You will now find your new ingestion source running

ingestion_running

Validate Ingestion Runs​

  1. View the latest status of ingestion runs on the Ingestion page

ingestion succeeded

  1. Click the plus sign to expand the full list of historical runs and outcomes; click Details to see the outcomes of a specific run

ingestion_details

  1. From the Ingestion Run Details page, pick View All to see which entities were ingested

ingestion_details_view_all

  1. Pick an entity from the list to manually validate if it contains the detail you expected

ingestion_details_view_all

Congratulations! You've successfully set up Looker & LookML as an ingestion source for DataHub!

Need more help? Join the conversation in Slack!