Connect to and manage Google BigQuery projects in Azure Purview (Preview)

This article outlines how to register Google BigQuery projects, and how to authenticate and interact with Google BigQuery in Azure Purview. For more information about Azure Purview, read the introductory article.

Important

Google BigQuery as a source is currently in PREVIEW. The Supplemental Terms of Use for Microsoft Azure Previews include additional legal terms that apply to Azure features that are in beta, preview, or otherwise not yet released into general availability.

Supported capabilities

Metadata Extraction Full Scan Incremental Scan Scoped Scan Classification Access Policy Lineage
Yes Yes No No No No Yes

Important

Supported Google BigQuery version is 11.0.0.

Prerequisites

Register

This section describes how to register a Google BigQuery project in Azure Purview using the Purview Studio.

Steps to register

  1. Navigate to your Purview account.

  2. Select Data Map on the left navigation.

  3. Select Register.

  4. On Register sources, select Google BigQuery . Select Continue.

    register BigQuery source

On the Register sources (Google BigQuery) screen, do the following:

  1. Enter a Name that the data source will be listed within the Catalog.

  2. Enter the ProjectID. This should be a fully qualified project ID. For example, mydomain.com:myProject

  3. Select a collection or create a new one (Optional)

  4. Select Register.

    configure BigQuery source

Scan

Follow the steps below to scan a Google BigQuery project to automatically identify assets and classify your data. For more information about scanning in general, see our introduction to scans and ingestion.

Create and run scan

  1. In the Management Center, select Integration runtimes. Make sure a self-hosted integration runtime is set up. If it is not set up, use the steps mentioned here.

  2. Navigate to Sources.

  3. Select the registered BigQuery project.

  4. Select + New scan.

  5. Provide the below details:

    1. Name: The name of the scan

    2. Connect via integration runtime: Select the configured self-hosted integration runtime

    3. Credential: While configuring BigQuery credential, make sure to:

      • Select Basic Authentication as the Authentication method
      • Provide the email ID of the service account in the User name field. For example, xyz\@developer.gserviceaccount.com
      • Follow below steps to generate the private key, copy the JSON then store it as the value of a Key Vault secret.

      To create a new private key from Google's cloud platform:

      1. In the navigation menu, select IAM & Admin -> Service Accounts -> Select a project ->
      2. Select the email address of the service account that you want to create a key for.
      3. Select the Keys tab.
      4. Select the Add key drop-down menu, then select Create new key.
      5. Choose JSON format.

      Note

      The contents of the private key are saved in a temp file on the VM when scanning processes are running. This temp file is deleted after the scans are successfully completed. In the event of a scan failure, the system will continue to retry until success. Please make sure access is appropriately restricted on the VM where SHIR is running.

      To understand more on credentials, refer to the link here.

    4. Driver location: Specify the path to the JDBC driver location in your VM where self-host integration runtime is running. This should be the path to valid JAR folder location.

      Note

      The driver should be accessible to all accounts in the VM.Please do not install in a user account.

    5. Dataset: Specify a list of BigQuery datasets to import. For example, dataset1; dataset2. When the list is empty, all available datasets are imported. Acceptable dataset name patterns using SQL LIKE expressions syntax include using %.

      Example: A%; %B; %C%; D

      • Start with A or
      • end with B or
      • contain C or
      • equal D

      Usage of NOT and special characters are not acceptable.

    6. Maximum memory available: Maximum memory (in GB) available on your VM to be used by scanning processes. This is dependent on the size of Google BigQuery project to be scanned.

      scan BigQuery source

  6. Select Test connection.

  7. Select Continue.

  8. Choose your scan trigger. You can set up a schedule or ran the scan once.

  9. Review your scan and select Save and Run.

View your scans and scan runs

To view existing scans, do the following:

  1. Go to the Purview Studio. Select the Data Map tab under the left pane.

  2. Select the desired data source. You will see a list of existing scans on that data source under Recent scans, or can view all scans under the Scans tab.

  3. Select the scan that has results you want to view.

  4. This page will show you all of the previous scan runs along with the status and metrics for each scan run. It will also display whether your scan was scheduled or manual, how many assets had classifications applied, how many total assets were discovered, the start and end time of the scan, and the total scan duration.

Manage your scans - edit, delete, or cancel

To manage or delete a scan, do the following:

  1. Go to the Purview Studio. Select the Data Map tab under the left pane.

  2. Select the desired data source. You will see a list of existing scans on that data source under Recent scans, or can view all scans under the Scans tab.

  3. Select the scan you would like to manage. You can edit the scan by selecting Edit scan.

  4. You can cancel an in progress scan by selecting Cancel scan run.

  5. You can delete your scan by selecting Delete scan.

Note

  • Deleting your scan does not delete catalog assets created from previous scans.
  • The asset will no longer be updated with schema changes if your source table has changed and you re-scan the source table after editing the description in the schema tab of Purview.

Next steps

Now that you have registered your source, follow the below guides to learn more about Purview and your data.