Share via

Connect to and manage Google BigQuery projects in Microsoft Purview

  • Artikel

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

Supported capabilities

Metadata Extraction Full Scan Incremental Scan Scoped Scan Classification Labeling Access Policy Lineage Data Sharing Live view
Yes Yes No Yes No No No Yes No No

When scanning Google BigQuery source, Microsoft Purview supports:

  • Extracting technical metadata including:

    • Projects
    • Datasets
    • Tables including the columns
    • Views including the columns

  • Fetching static lineage on assets relationships among tables and views.

When setting up scan, you can choose to scan an entire Google BigQuery project, or scope the scan to a subset of datasets matching the given name(s) or name pattern(s).

Known limitations

  • Currently, Microsoft Purview only supports scanning Google BigQuery datasets in US multi-regional location. If the specified dataset is in other location e.g. us-east1 or EU, you will observe scan completes but no assets shown up in Microsoft Purview.
  • When object is deleted from the data source, currently the subsequent scan won't automatically remove the corresponding asset in Microsoft Purview.

Prerequisites

Required permissions for scan

The Google BigQuery service account you use for scan needs to have both BigQuery Metadata Viewer and BigQuery Job User IAM roles on the project(s) you want to scan. These permissions are required because Microsoft Purview extracts the metadata by reading the Google BigQuery database system tables (e.g. INFORMATION_SCHEMA). And the underlying Google BigQuery JDBC driver creates a BigQuery job when it needs to read from these system tables.

Microsoft Purview uses Oauth 2.0 protocol for accessing Google BigQuery service. Set up the credential by following the instruction in Create and run scan section.

Register

This section describes how to register a Google BigQuery project in Microsoft Purview using the Microsoft Purview governance portal.

Steps to register

  1. Open the Microsoft Purview governance portal by:

  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 from the list.

  4. Select Register.

    configure BigQuery source

Scan

Follow the steps below to scan a Google BigQuery project to automatically identify assets. 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 isn't set up, use the steps mentioned in prerequisites.

  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 entire JSON key file 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 machine where self-host integration runtime is running. For example: D:\Drivers\GoogleBigQuery.

      1. For self-hosted integration runtime on a local machine: D:\Drivers\GoogleBigQuery. It's the path to valid JAR folder location. The value must be a valid absolute file path and doesn't contain space. Make sure the driver is accessible by the self-hosted integration runtime;; learn more from prerequisites section.
      2. For Kubernetes-supported self-hosted integration runtime: ./drivers/GoogleBigQuery. It's the path to valid JAR folder location. The value must be a valid relative file path. Please refer to the documentation to set up a scan with external drivers for uploading drivers in advance.

    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 can be static names or contain wildcard %.

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

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

      Usage of NOT and special characters aren't 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:

  1. Go to the Microsoft Purview portal. On the left pane, select Data map.
  2. Select the data source. You can view a list of existing scans on that data source under Recent scans, or you can view all scans on the Scans tab.
  3. Select the scan that has results you want to view. The pane shows you all the previous scan runs, along with the status and metrics for each scan run.
  4. Select the run ID to check the scan run details.

Manage your scans

To edit, cancel, or delete a scan:

  1. Go to the Microsoft Purview portal. On the left pane, select Data Map.

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

  3. Select the scan that you want to manage. You can then:

    • Edit the scan by selecting Edit scan.
    • Cancel an in-progress scan by selecting Cancel scan run.
    • Delete your scan by selecting Delete scan.

Note

  • Deleting your scan does not delete catalog assets created from previous scans.

Lineage

After scanning your Google BigQuery source, you can browse data catalog or search data catalog to view the asset details.

Go to the asset -> lineage tab, you can see the asset relationship when applicable. Refer to the supported capabilities section on the supported Google BigQuery lineage scenarios. For more information about lineage in general, see data lineage and lineage user guide.

Google BigQuery lineage view

Next steps

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