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148 lines
6.1 KiB
Markdown
148 lines
6.1 KiB
Markdown
---
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title: Configuration
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---
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# Configuring Your BigQuery Connector to DataHub
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Now that you have created a Service Account and Service Account Key in BigQuery in [the prior step](setup.md), it's now time to set up a connection via the DataHub UI.
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## Configure Secrets
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1. Within DataHub, navigate to the **Ingestion** tab in the top, right corner of your screen
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<p align="center">
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<img width="75%" alt="Navigate to the "Ingestion Tab"" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/guides/bigquery/bigquery_ingestion_ingestion_button.png"/>
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</p>
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:::note
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If you do not see the Ingestion tab, please contact your DataHub admin to grant you the correct permissions
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:::
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2. Navigate to the **Secrets** tab and click **Create new secret**
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<p align="center">
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<img width="75%" alt="Secrets Tab" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/guides/bigquery/bigquery_ingestion_secrets_tab.png"/>
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</p>
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3. Create a Private Key secret
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This will securely store your BigQuery Service Account Private Key within DataHub
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- Enter a name like `BIGQUERY_PRIVATE_KEY` - we will use this later to refer to the secret
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- Copy and paste the `private_key` value from your Service Account Key
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- Optionally add a description
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- Click **Create**
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<p align="center">
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<img width="75%" alt="Private Key Secret" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/guides/bigquery/bigquery_ingestion_private_key_secret.png"/>
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</p>
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4. Create a Private Key ID secret
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This will securely store your BigQuery Service Account Private Key ID within DataHub
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- Click **Create new secret** again
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- Enter a name like `BIGQUERY_PRIVATE_KEY_ID` - we will use this later to refer to the secret
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- Copy and paste the `private_key_id` value from your Service Account Key
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- Optionally add a description
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- Click **Create**
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<p align="center">
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<img width="75%" alt="Private Key Id Secret" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/guides/bigquery/bigquery_ingestion_private_key_id_secret.png"/>
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</p>
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## Configure Recipe
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5. Navigate to the **Sources** tab and click **Create new source**
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<p align="center">
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<img width="75%" alt="Click "Create new source"" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/guides/bigquery/bigquery_ingestion_click_create_new_source_button.png"/>
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</p>
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6. Select BigQuery
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<p align="center">
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<img width="75%" alt="Select BigQuery from the options" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/guides/bigquery/bigquery_ingestion_bigquery_button.png"/>
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</p>
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7. Fill out the BigQuery Recipe
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You can find the following details in your Service Account Key file:
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- Project ID
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- Client Email
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- Client ID
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Populate the Secret Fields by selecting the Private Key and Private Key ID secrets you created in steps 3 and 4.
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<p align="center">
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<img width="75%" alt="Fill out the BigQuery Recipe" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/guides/bigquery/bigquery-ingestion-recipe.png"/>
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</p>
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8. Click **Test Connection**
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This step will ensure you have configured your credentials accurately and confirm you have the required permissions to extract all relevant metadata.
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<p align="center">
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<img width="75%" alt="Test BigQuery connection" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/guides/bigquery/bigquery-test-connection.png"/>
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</p>
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After you have successfully tested your connection, click **Next**.
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## Schedule Execution
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Now it's time to schedule a recurring ingestion pipeline to regularly extract metadata from your BigQuery instance.
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9. Decide how regularly you want this ingestion to run-- day, month, year, hour, minute, etc. Select from the dropdown
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<p align="center">
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<img width="75%" alt="schedule selector" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/guides/bigquery/bigquery_ingestion_scheduled_execution.png"/>
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</p>
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10. Ensure you've configured your correct timezone
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<p align="center">
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<img width="75%" alt="timezone_selector" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/guides/bigquery/bigquery_ingestion_timezone_selector.png"/>
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</p>
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11. Click **Next** when you are done
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## Finish Up
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12. Name your ingestion source, then click **Save and Run**
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<p align="center">
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<img width="75%" alt="Name your ingestion" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/guides/bigquery/bigquery_ingestion_name_ingestion.png"/>
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</p>
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You will now find your new ingestion source running
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<p align="center">
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<img width="75%" alt="ingestion_running" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/guides/bigquery/bigquery_ingestion_running.png"/>
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</p>
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## Validate Ingestion Runs
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13. View the latest status of ingestion runs on the Ingestion page
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<p align="center">
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<img width="75%" alt="ingestion succeeded" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/guides/bigquery/bigquery_ingestion_succeeded.png"/>
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</p>
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14. Click the plus sign to expand the full list of historical runs and outcomes; click **Details** to see the outcomes of a specific run
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<p align="center">
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<img width="75%" alt="ingestion_details" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/guides/bigquery/bigquery_ingestion_details.png"/>
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</p>
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15. From the Ingestion Run Details page, pick **View All** to see which entities were ingested
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<p align="center">
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<img width="75%" alt="ingestion_details_view_all" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/guides/bigquery/bigquery_ingestion_details_view_all.png"/>
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</p>
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16. Pick an entity from the list to manually validate if it contains the detail you expected
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<p align="center">
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<img width="75%" alt="ingestion_details_view_all" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/guides/bigquery/bigquery_ingestion_ingested_assets.png"/>
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</p>
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**Congratulations!** You've successfully set up BigQuery as an ingestion source for DataHub!
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