# Profile your data Data profile scans help you better understand your data by analyzing its statistical characteristics, such as average, unique, and maximum values. Using these Knowledge Catalog-powered scans lets you uncover deep insights about your datasets and quickly establish [recommend rules for data quality checks](https://docs.cloud.google.com/knowledge-catalog/docs/auto-data-quality-overview.md.txt). For more information about data profiling, see [About data profiling](https://docs.cloud.google.com/knowledge-catalog/docs/data-profiling-overview.md.txt). > [!TIP] > **Tip:** This document explains how to create and manage data profile scans when working with a specific table in BigQuery. To manage data profile scans across your project, use Knowledge Catalog (see [Create and use data profile scans](https://docs.cloud.google.com/knowledge-catalog/docs/use-data-profiling.md.txt)). ## Before you begin Enable the Dataplex API, if it is not already enabled. **Roles required to enable APIs** To enable APIs, you need the `serviceusage.services.enable` permission. If you created the project, then you likely already have this permission through the Owner role (`roles/owner`). Otherwise, you can get this permission through the Service Usage Admin role (`roles/serviceusage.serviceUsageAdmin`). [Learn how to grant roles](https://docs.cloud.google.com/iam/docs/granting-changing-revoking-access.md.txt). [Enable the API](https://console.cloud.google.com/apis/enableflow?apiid=dataplex.googleapis.com) ## Required roles This section describes the IAM roles and permissions needed to use Knowledge Catalog data profile scans. ### User roles and permissions To get the permissions that you need to create and manage data profile scans, ask your administrator to grant you the following IAM roles: - Create, run, update, and delete data profile scans: [Dataplex DataScan Editor](https://docs.cloud.google.com/iam/docs/roles-permissions/dataplex.md.txt#dataplex.dataScanEditor) (`roles/dataplex.dataScanEditor`) on the project containing the data scan - View data profile scan results, jobs, and history: [Dataplex DataScan Viewer](https://docs.cloud.google.com/iam/docs/roles-permissions/dataplex.md.txt#dataplex.dataScanViewer) (`roles/dataplex.dataScanViewer`) on the project containing the data scan - Publish data profile scan results to Knowledge Catalog: [Dataplex Catalog Editor](https://docs.cloud.google.com/iam/docs/roles-permissions/dataplex.md.txt#dataplex.catalogEditor) (`roles/dataplex.catalogEditor`) on the `@bigquery` entry group - View published data profile scan results in BigQuery on the **Data profile** tab: [BigQuery Data Viewer](https://docs.cloud.google.com/iam/docs/roles-permissions/bigquery.md.txt#bigquery.dataViewer) (`roles/bigquery.dataViewer`) on the table - Run data profile scans: - [BigQuery Job User](https://docs.cloud.google.com/iam/docs/roles-permissions/bigquery.md.txt#bigquery.jobUser) (`roles/bigquery.jobUser`) on the project running the scan (all table types) - [BigQuery Data Viewer](https://docs.cloud.google.com/iam/docs/roles-permissions/bigquery.md.txt#bigquery.dataViewer) (`roles/bigquery.dataViewer`) on the BigQuery tables being scanned - Run data profile scans against BigQuery external tables that use Cloud Storage data: - [Storage Object Viewer](https://docs.cloud.google.com/iam/docs/roles-permissions/storage.md.txt#storage.objectViewer) (`roles/storage.objectViewer`) on the Cloud Storage bucket (Cloud Storage, Apache Hive catalog endpoint, and Apache Iceberg REST catalog endpoint) - [Storage Legacy Bucket Reader](https://docs.cloud.google.com/iam/docs/roles-permissions/storage.md.txt#storage.legacyBucketReader) (`roles/storage.legacyBucketReader`) on the Cloud Storage bucket - Run data profile scans for Apache Iceberg, Apache Hive, and SAP BDC Delta Lake tables managed by Google Cloud Lakehouse: [BigLake Viewer](https://docs.cloud.google.com/iam/docs/roles-permissions/biglake.md.txt#biglake.viewer) (`roles/biglake.viewer`) on the tables being scanned - Export data profile scan results to a BigQuery table: [BigQuery Data Editor](https://docs.cloud.google.com/iam/docs/roles-permissions/bigquery.md.txt#bigquery.dataEditor) (`roles/bigquery.dataEditor`) on the table For more information about granting roles, see [Manage access to projects, folders, and organizations](https://docs.cloud.google.com/iam/docs/granting-changing-revoking-access.md.txt). These predefined roles contain the permissions required to create and manage data profile scans. To see the exact permissions that are required, expand the **Required permissions** section: #### Required permissions The following permissions are required to create and manage data profile scans: - Create, run, update, and delete data profile scans: - `dataplex.datascans.create` on project - `dataplex.datascans.update` on data scan - `dataplex.datascans.delete` on data scan - `dataplex.datascans.run` on data scan - `dataplex.datascans.get` on data scan - `dataplex.datascans.list` on project - `dataplex.dataScanJobs.get` on data scan job - `dataplex.dataScanJobs.list` on data scan - View data profile scan results, jobs, and history: - `dataplex.datascans.getData` on data scan - `dataplex.datascans.list` on project - `dataplex.dataScanJobs.get` on data scan job - `dataplex.dataScanJobs.list` on data scan - Publish data profile scan results to Knowledge Catalog: - `dataplex.entryGroups.useDataProfileAspect` on entry group - `bigquery.tables.update` on table - `dataplex.entries.update` on entry - View published data profile results for a table in BigQuery or Knowledge Catalog: - `bigquery.tables.get` on table - `bigquery.tables.getData` on table You might also be able to get these permissions with [custom roles](https://docs.cloud.google.com/iam/docs/creating-custom-roles.md.txt) or other [predefined roles](https://docs.cloud.google.com/iam/docs/roles-overview.md.txt#predefined). ### Knowledge Catalog service account roles and permissions Whichever [execution identity](https://docs.cloud.google.com/knowledge-catalog/docs/data-profiling-overview.md.txt#execution_identity) you select (the default Knowledge Catalog Service Agent, a custom service account, or End-User Credentials), that identity requires the following roles and permissions to run the data profile scan jobs in the backend and export results. > [!NOTE] > **Note:** If you're scanning CMEK-encrypted BigQuery resources, you must also grant the `roles/cloudkms.cryptoKeyEncrypterDecrypter` role to the relevant service agents. For more information, see [DataScans CMEK permissions](https://docs.cloud.google.com/knowledge-catalog/docs/cmek.md.txt#datascans-cmek-permissions). To ensure that the execution identity has the necessary permissions to run data profile scans and export results, ask your administrator to grant the following IAM roles to the execution identity: **Important:** You must grant these roles to the execution identity, *not* to your user account. Failure to grant the roles to the correct principal might result in permission errors. - Run data profile scans: - [BigQuery Job User](https://docs.cloud.google.com/iam/docs/roles-permissions/bigquery.md.txt#bigquery.jobUser) (`roles/bigquery.jobUser`) on the project running the scan (all table types) - [BigQuery Data Viewer](https://docs.cloud.google.com/iam/docs/roles-permissions/bigquery.md.txt#bigquery.dataViewer) (`roles/bigquery.dataViewer`) on the BigQuery tables being scanned - Run data profile scans for BigQuery external tables that use Cloud Storage data: - [Storage Object Viewer](https://docs.cloud.google.com/iam/docs/roles-permissions/storage.md.txt#storage.objectViewer) (`roles/storage.objectViewer`) on Cloud Storage bucket - [Storage Legacy Bucket Reader](https://docs.cloud.google.com/iam/docs/roles-permissions/storage.md.txt#storage.legacyBucketReader) (`roles/storage.legacyBucketReader`) on the Cloud Storage bucket - Run data profile scans for Apache Iceberg, Apache Hive, and SAP BDC Delta Lake tables managed by Google Cloud Lakehouse: [BigLake Viewer](https://docs.cloud.google.com/iam/docs/roles-permissions/biglake.md.txt#biglake.viewer) (`roles/biglake.viewer`) on the tables being scanned - Export data profile scan results to a BigQuery table: [BigQuery Data Editor](https://docs.cloud.google.com/iam/docs/roles-permissions/bigquery.md.txt#bigquery.dataEditor) (`roles/bigquery.dataEditor`) on the table For more information about granting roles, see [Manage access to projects, folders, and organizations](https://docs.cloud.google.com/iam/docs/granting-changing-revoking-access.md.txt). These predefined roles contain the permissions required to run data profile scans and export results. To see the exact permissions that are required, expand the **Required permissions** section: #### Required permissions The following permissions are required to run data profile scans and export results: - Run data profile scans against BigQuery data: - `bigquery.jobs.create` on project - `bigquery.tables.get` on table - `bigquery.tables.getData` on table - Run data profile scans for BigQuery external tables that use Cloud Storage data: - `storage.buckets.get` on bucket - `storage.objects.get` on object - Export data profile scan results to a BigQuery table: - `bigquery.tables.create` on dataset - `bigquery.tables.updateData` on table Your administrator might also be able to give the execution identity these permissions with [custom roles](https://docs.cloud.google.com/iam/docs/creating-custom-roles.md.txt) or other [predefined roles](https://docs.cloud.google.com/iam/docs/roles-overview.md.txt#predefined). If a table uses BigQuery [row-level security](https://docs.cloud.google.com/bigquery/docs/row-level-security-intro), then Knowledge Catalog can only scan rows visible to the Knowledge Catalog service account. To let Knowledge Catalog scan all rows, add its service account to a row filter where the predicate is `TRUE`. If a table uses BigQuery [column-level security](https://docs.cloud.google.com/bigquery/docs/column-level-security.md.txt), then Knowledge Catalog requires access to scan protected columns. To grant access, give the Knowledge Catalog service account the **Data Catalog Fine-Grained Reader** (`roles/datacatalog.fineGrainedReader`) role on all policy tags used in the table. The user creating or updating a data scan also needs permissions on protected columns. ### Grant roles to the Knowledge Catalog service account To run data profile scans, Knowledge Catalog uses a service account that requires permissions to run BigQuery jobs and read BigQuery table data. To grant the required roles, follow these steps: 1. Get the Knowledge Catalog service account email address. If you haven't created a data profile or data quality scan in this project before, run the following `gcloud` command to generate the service identity: ```bash gcloud beta services identity create --service=dataplex.googleapis.com ``` The command returns the service account email, which has the following format: service-PROJECT_ID@gcp-sa-dataplex.iam.gserviceaccount.com. If the service account already exists, you can find its email by viewing principals with the **Dataplex** name on the [**IAM** page](https://console.cloud.google.com/iam-admin/iam) in the Google Cloud console. 2. Grant the service account the **BigQuery Job User** (`roles/bigquery.jobUser`) role on your project. This role lets the service account run BigQuery jobs for the scan. ```bash gcloud projects add-iam-policy-binding PROJECT_ID \ --member="serviceAccount:service-PROJECT_NUMBER@gcp-sa-dataplex.iam.gserviceaccount.com" \ --role="roles/bigquery.jobUser" ``` Replace the following: - `PROJECT_ID`: your Google Cloud project ID. - `service-PROJECT_NUMBER@gcp-sa-dataplex.iam.gserviceaccount.com`: the email of the Knowledge Catalog service account. 3. Grant the service account the **BigQuery Data Viewer** (`roles/bigquery.dataViewer`) role for each table that you want to profile. This role grants read-only access to the tables. ```bash gcloud bigquery tables add-iam-policy-binding DATASET_ID.TABLE_ID \ --member="serviceAccount:service-PROJECT_NUMBER@gcp-sa-dataplex.iam.gserviceaccount.com" \ --role="roles/bigquery.dataViewer" ``` Replace the following: - `DATASET_ID`: the ID of the dataset containing the table. - `TABLE_ID`: the ID of the table to profile. - `service-PROJECT_NUMBER@gcp-sa-dataplex.iam.gserviceaccount.com`: the email of the Knowledge Catalog service account.
## Create a data profile scan ### Console 1. In the Google Cloud console, go to the Knowledge Catalog **Data profiling \& quality** page. [Go to Data profiling \& quality](https://console.cloud.google.com/dataplex/govern/data-profiling-and-quality)
2. Click **Create data profile scan**. 3. Optional: Enter a **Display name**. 4. Enter an **ID** . See the [Resource naming conventions](https://docs.cloud.google.com/compute/docs/naming-resources.md.txt#resource-name-format). 5. Optional: Enter a **Description**. 6. In the **Table** field, click **Browse** . Choose the table to scan, and then click **Select**. Only standard BigQuery tables and Apache Iceberg, Apache Hive, and SAP BDC Delta Lake tables managed by Google Cloud Lakehouse are supported. For tables in multi-region datasets, choose a region where to create the data scan. To browse the tables organized within Knowledge Catalog lakes, click **Browse within Knowledge Catalog Lakes**. 7. In the **Mode** section, select one of the following options: - **Standard**: profiles your data with customizable scan settings. This is the default mode. - **Lightweight**: provides quick insights with a low-latency, low-fidelity scan. 8. If you chose the **Standard** mode, configure the following options. These options don't appear when you select **Lightweight** mode. 1. In the **Scope** field, choose **Incremental** or **Entire data**. If you choose **Incremental data** , in the **Timestamp column** field, select a column of type `DATE` or `TIMESTAMP` from your BigQuery table. Knowledge Catalog uses this column to identify new records as they're added. For tables partitioned on a column of type `DATE` or `TIMESTAMP`, it's recommended to use this column as the partition column. 2. Optional: To filter your data, do any of the following: - To filter by rows, select the **Filter rows** checkbox. Enter a valid SQL expression that can be used in a [`WHERE` clause in GoogleSQL syntax](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax.md.txt#where_clause). For example: `col1 >= 0`. The filter can be a combination of SQL conditions over multiple columns. For example: `col1 >= 0 AND col2 < 10`. - To filter by columns, select the **Filter columns** checkbox. - To include columns in the profile scan, in the **Include columns** field, click **Browse** . Select the columns to include, and then click **Select**. - To exclude columns from the profile scan, in the **Exclude columns** field, click **Browse** . Select the columns to exclude, and then click **Select**. > [!NOTE] > **Note:** You can use **Include columns** , **Exclude columns** , or both. If you use both the fields, then the data profile scan first selects the columns based on your input in the **Include columns** field and then excludes the columns based on your input in the **Exclude columns** field. 3. To apply sampling to your data profile scan, in the **Sampling size** list, select a sampling percentage. Choose a percentage value that ranges between 0.0% and 100.0% with up to 3 decimal digits. - For larger datasets, choose a lower sampling percentage. For example, for a 1 PB table, if you enter a value between 0.1% and 1.0%, the data profile samples between 1-10 TB of data. - There must be at least 100 records in the sampled data to return a result. - For incremental data scans, the data profile scan applies sampling to the latest increment. 9. Optional: Publish the data profile scan results in the BigQuery and Knowledge Catalog pages in the Google Cloud console for the source table. Select the **Publish results to Knowledge Catalog** checkbox. You can view the latest scan results in the **Data profile** tab in the BigQuery and Knowledge Catalog pages for the source table. To let users access the published scan results, see the [Grant access to data profile scan results](https://docs.cloud.google.com/bigquery/docs/data-profile-scan.md.txt#share-results) section of this document. The publishing option might not be available in the following cases: - You don't have the required permissions on the table. - Another data profile scan is set to publish results. 10. In the **Schedule** section, choose one of the following options: - **Repeat** : Run the data profile scan on a schedule: hourly, daily, weekly, monthly, or custom. Specify how often the scan should run and at what time. If you choose custom, use [cron](https://en.wikipedia.org/wiki/Cron) format to specify the schedule. - **On-demand**: Run the data profile scan on demand. - **One-time run** : Run the data profile scan once now, and remove the scan after the auto-deletion time. This feature's in [Preview](https://cloud.google.com/products/#product-launch-stages). - **Set post-scan results auto-deletion**: The auto-deletion time defines the duration a data profile scan remains active after execution. A data profile scan without a specified auto-deletion time is automatically removed after 24 hours. The auto-deletion time can range from 0 seconds (immediate deletion) to 365 days. 11. Click **Continue**. 12. Optional: Export the scan results to a BigQuery standard table. In the **Export scan results to BigQuery table** section, do the following: 1. In the **Select BigQuery dataset** field, click **Browse**. Select a BigQuery dataset to store the data profile scan results. 2. In the **BigQuery table** field, specify the table to store the data profile scan results. If you're using an existing table, make sure that it's compatible with the [export table schema](https://docs.cloud.google.com/knowledge-catalog/docs/use-data-profiling.md.txt#table-schema). If the specified table doesn't exist, Knowledge Catalog creates it for you. > [!NOTE] > **Note:** You can use the same results table for multiple data profile scans. 13. Optional: Add labels. Labels are key-value pairs that let you group related objects together or with other Google Cloud resources. 14. To create the scan, click **Create**. If you set the schedule to **On-demand** , you can also run the scan now by clicking **Run scan**. ### gcloud To create a data profile scan, use the [`gcloud dataplex datascans create data-profile` command](https://docs.cloud.google.com/sdk/gcloud/reference/dataplex/datascans/create/data-profile.md.txt). If the source data is organized in a Knowledge Catalog lake, include the `--data-source-entity` flag: ``` gcloud dataplex datascans create data-profile DATASCAN \ --location=LOCATION \ --data-source-entity=DATA_SOURCE_ENTITY ``` If the source data isn't organized in a Knowledge Catalog lake, include the `--data-source-resource` flag: ``` gcloud dataplex datascans create data-profile DATASCAN \ --location=LOCATION \ --data-source-resource=DATA_SOURCE_RESOURCE ``` Replace the following variables: - `DATASCAN`: The ID of the data profile scan. - `LOCATION`: The Google Cloud region in which to create the data profile scan. - `DATA_SOURCE_ENTITY`: The Knowledge Catalog entity that contains the data for the data profile scan. For example, `projects/test-project/locations/test-location/lakes/test-lake/zones/test-zone/entities/test-entity`. - `DATA_SOURCE_RESOURCE`: The name of the resource that contains the data for the data profile scan. For example, `//bigquery.googleapis.com/projects/test-project/datasets/test-dataset/tables/test-table`. ### C# ### C# Before trying this sample, follow the C# setup instructions in the [Knowledge Catalog quickstart using client libraries](https://docs.cloud.google.com/knowledge-catalog/docs/reference/libraries). For more information, see the [Knowledge Catalog C# API reference documentation](https://docs.cloud.google.com/dotnet/docs/reference/Google.Cloud.Dataplex.V1/latest). To authenticate to Knowledge Catalog, set up Application Default Credentials. For more information, see [Set up authentication for a local development environment](https://docs.cloud.google.com/docs/authentication/set-up-adc-local-dev-environment.md.txt). ```c# using Google.Api.Gax.ResourceNames; using Google.Cloud.Dataplex.V1; using Google.LongRunning; public sealed partial class GeneratedDataScanServiceClientSnippets { /// Snippet for CreateDataScan /// /// This snippet has been automatically generated and should be regarded as a code template only. /// It will require modifications to work: /// - It may require correct/in-range values for request initialization. /// - It may require specifying regional endpoints when creating the service client as shown in /// https://cloud.google.com/dotnet/docs/reference/help/client-configuration#endpoint. /// public void CreateDataScanRequestObject() { // Create client DataScanServiceClient dataScanServiceClient = DataScanServiceClient.Create(); // Initialize request argument(s) CreateDataScanRequest request = new CreateDataScanRequest { ParentAsLocationName = LocationName.FromProjectLocation("[PROJECT]", "[LOCATION]"), DataScan = new DataScan(), DataScanId = "", ValidateOnly = false, }; // Make the request Operation response = dataScanServiceClient.CreateDataScan(request); // Poll until the returned long-running operation is complete Operation completedResponse = response.PollUntilCompleted(); // Retrieve the operation result DataScan result = completedResponse.Result; // Or get the name of the operation string operationName = response.Name; // This name can be stored, then the long-running operation retrieved later by name Operation retrievedResponse = dataScanServiceClient.PollOnceCreateDataScan(operationName); // Check if the retrieved long-running operation has completed if (retrievedResponse.IsCompleted) { // If it has completed, then access the result DataScan retrievedResult = retrievedResponse.Result; } } } ```
### Go ### Go Before trying this sample, follow the Go setup instructions in the [Knowledge Catalog quickstart using client libraries](https://docs.cloud.google.com/knowledge-catalog/docs/reference/libraries). For more information, see the [Knowledge Catalog Go API reference documentation](https://pkg.go.dev/cloud.google.com/go/dataplex). To authenticate to Knowledge Catalog, set up Application Default Credentials. For more information, see [Set up authentication for a local development environment](https://docs.cloud.google.com/docs/authentication/set-up-adc-local-dev-environment.md.txt). ```go //go:build examples package main import ( "context" dataplex "cloud.google.com/go/dataplex/apiv1" dataplexpb "cloud.google.com/go/dataplex/apiv1/dataplexpb" ) func main() { ctx := context.Background() // This snippet has been automatically generated and should be regarded as a code template only. // It will require modifications to work: // - It may require correct/in-range values for request initialization. // - It may require specifying regional endpoints when creating the service client as shown in: // https://pkg.go.dev/cloud.google.com/go#hdr-Client_Options c, err := dataplex.NewDataScanClient(ctx) if err != nil { // TODO: Handle error. } defer c.Close() req := &dataplexpb.CreateDataScanRequest{ // TODO: Fill request struct fields. // See https://pkg.go.dev/cloud.google.com/go/dataplex/apiv1/dataplexpb#CreateDataScanRequest. } op, err := c.CreateDataScan(ctx, req) if err != nil { // TODO: Handle error. } resp, err := op.Wait(ctx) if err != nil { // TODO: Handle error. } // TODO: Use resp. _ = resp } ``` ### Java ### Java Before trying this sample, follow the Java setup instructions in the [Knowledge Catalog quickstart using client libraries](https://docs.cloud.google.com/knowledge-catalog/docs/reference/libraries). For more information, see the [Knowledge Catalog Java API reference documentation](https://docs.cloud.google.com/java/docs/reference/google-cloud-dataplex/latest/overview). To authenticate to Knowledge Catalog, set up Application Default Credentials. For more information, see [Set up authentication for a local development environment](https://docs.cloud.google.com/docs/authentication/set-up-adc-local-dev-environment.md.txt). ```java import com.google.cloud.dataplex.v1.CreateDataScanRequest; import com.google.cloud.dataplex.v1.DataScan; import com.google.cloud.dataplex.v1.DataScanServiceClient; import com.google.cloud.dataplex.v1.LocationName; public class SyncCreateDataScan { public static void main(String[] args) throws Exception { syncCreateDataScan(); } public static void syncCreateDataScan() throws Exception { // This snippet has been automatically generated and should be regarded as a code template only. // It will require modifications to work: // - It may require correct/in-range values for request initialization. // - It may require specifying regional endpoints when creating the service client as shown in // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library try (DataScanServiceClient dataScanServiceClient = DataScanServiceClient.create()) { CreateDataScanRequest request = CreateDataScanRequest.newBuilder() .setParent(LocationName.of("[PROJECT]", "[LOCATION]").toString()) .setDataScan(DataScan.newBuilder().build()) .setDataScanId("dataScanId1260787906") .setValidateOnly(true) .build(); DataScan response = dataScanServiceClient.createDataScanAsync(request).get(); } } } ``` ### Python ### Python Before trying this sample, follow the Python setup instructions in the [Knowledge Catalog quickstart using client libraries](https://docs.cloud.google.com/knowledge-catalog/docs/reference/libraries). For more information, see the [Knowledge Catalog Python API reference documentation](https://docs.cloud.google.com/python/docs/reference/dataplex/latest). To authenticate to Knowledge Catalog, set up Application Default Credentials. For more information, see [Set up authentication for a local development environment](https://docs.cloud.google.com/docs/authentication/set-up-adc-local-dev-environment.md.txt). ```python # Copyright 2026 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import google.api_core.exceptions from google.cloud import dataplex_v1 def create_data_profile_scan_global( project_id: str, dataset_id: str, table_id: str, location: str, ) -> None: """Creates a Dataplex Data Profile Scan using global API endpoint routing. Args: project_id (str): Google Cloud project ID where the scan is created. dataset_id (str): Target BigQuery dataset ID. table_id (str): Target BigQuery table ID to scan. location (str): Google Cloud region where serverless compute runs. """ client = dataplex_v1.DataScanServiceClient() parent = client.common_location_path(project=project_id, location=location) bigquery_table = ( f"//bigquery.googleapis.com/projects/{project_id}" f"/datasets/{dataset_id}/tables/{table_id}" ) data_profile_spec = dataplex_v1.DataProfileSpec(sampling_percent=100.0) data_scan = dataplex_v1.DataScan( display_name="Global Data Profile Scan", description="Regional data profile scan generating automated table statistics.", data=dataplex_v1.DataSource(resource=bigquery_table), data_profile_spec=data_profile_spec, ) request = dataplex_v1.CreateDataScanRequest( parent=parent, data_scan=data_scan, ) try: operation = client.create_data_scan(request=request) print(operation.result()) except google.api_core.exceptions.AlreadyExists: print("A scan with this ID already exists.") except google.api_core.exceptions.InvalidArgument as e: print(f"Your scan configuration is invalid: {e}") except google.api_core.exceptions.GoogleAPIError as e: print(f"Unexpected exception: {e}") ``` ### Ruby ### Ruby Before trying this sample, follow the Ruby setup instructions in the [Knowledge Catalog quickstart using client libraries](https://docs.cloud.google.com/knowledge-catalog/docs/reference/libraries). For more information, see the [Knowledge Catalog Ruby API reference documentation](https://docs.cloud.google.com/ruby/docs/reference/google-cloud-dataplex/latest). To authenticate to Knowledge Catalog, set up Application Default Credentials. For more information, see [Set up authentication for a local development environment](https://docs.cloud.google.com/docs/authentication/set-up-adc-local-dev-environment.md.txt). ```ruby require "google/cloud/dataplex/v1" ## # Snippet for the create_data_scan call in the DataScanService service # # This snippet has been automatically generated and should be regarded as a code # template only. It will require modifications to work: # - It may require correct/in-range values for request initialization. # - It may require specifying regional endpoints when creating the service # client as shown in https://cloud.google.com/ruby/docs/reference. # # This is an auto-generated example demonstrating basic usage of # Google::Cloud::Dataplex::V1::DataScanService::Client#create_data_scan. # def create_data_scan # Create a client object. The client can be reused for multiple calls. client = Google::Cloud::Dataplex::V1::DataScanService::Client.new # Create a request. To set request fields, pass in keyword arguments. request = Google::Cloud::Dataplex::V1::CreateDataScanRequest.new # Call the create_data_scan method. result = client.create_data_scan request # The returned object is of type Gapic::Operation. You can use it to # check the status of an operation, cancel it, or wait for results. # Here is how to wait for a response. result.wait_until_done! timeout: 60 if result.response? p result.response else puts "No response received." end end ``` ### REST To create a data profile scan, use the [`dataScans.create` method](https://docs.cloud.google.com/knowledge-catalog/docs/reference/rest/v1/projects.locations.dataScans/create.md.txt). > [!NOTE] > **Note:** If your BigQuery table is configured with the `Require > partition filter` setting set to `true`, use the table's partition column as the data profile scan's row filter or timestamp column.
## Create multiple data profile scans You can configure data profile scans for multiple tables in a BigQuery dataset at the same time by using the Google Cloud console. 1. In the Google Cloud console, go to the Knowledge Catalog **Data profiling \& quality** page. [Go to Data profiling \& quality](https://console.cloud.google.com/dataplex/govern/data-profiling-and-quality)
2. Click **Create data profile scan**. 3. Select the **Multiple data profile scans** option. 4. Enter an **ID prefix**. Knowledge Catalog automatically generates scan IDs by using the provided prefix and unique suffixes. 5. Enter a **Description** for all of the data profile scans. 6. In the **Dataset** field, click **Browse** . Select a dataset to pick tables from. Click **Select**. 7. If the dataset is multi-regional, select a **Region** in which to create the data profile scans. 8. In the **Mode** section, choose one of the following options: - **Standard**: profiles your data with customizable scan settings. This is the default mode. - **Lightweight** : provides quick insights with a low-latency, low-fidelity scan. This feature is in [Preview](https://cloud.google.com/products#product-launch-stages). 9. If you chose the **Standard** mode, configure the following settings for the scans. These settings don't appear when **Lightweight** mode is selected. 1. In the **Scope** field, choose **Incremental** or **Entire data**. If you choose **Incremental** data, you can select only tables that are partitioned on a column of type `DATE` or `TIMESTAMP`. 2. To apply sampling to the data profile scans, in the **Sampling size** list, select a sampling percentage. Choose a percentage value between 0.0% and 100.0% with up to 3 decimal digits. 10. Optional: Publish the data profile scan results in the BigQuery and Knowledge Catalog pages in the Google Cloud console for the source table. Select the **Publish results to Knowledge Catalog** checkbox. You can view the latest scan results in the **Data profile** tab in the BigQuery and Knowledge Catalog pages for the source table. To let users access the published scan results, see the [Grant access to data profile scan results](https://docs.cloud.google.com/bigquery/docs/data-profile-scan#share-results) section of this document. > [!NOTE] > **Note:** You must choose tables that don't have any existing scans publishing their results. 11. In the **Schedule** section, choose one of the following options: - **Repeat** : Run the data profile scans on a schedule: hourly, daily, weekly, monthly, or custom. Specify how often the scans should run and at what time. If you choose custom, use [cron](https://en.wikipedia.org/wiki/Cron) format to specify the schedule. - **On-demand**: Run the data profile scans on demand. - **One-time run** : Run the data profile scan once now, and remove the scan after the auto-deletion time. This feature's in [Preview](https://cloud.google.com/products/#product-launch-stages). - **Set post-scan results auto-deletion**: The auto-deletion time defines the duration a data profile scan remains active after execution. A data profile scan without a specified auto-deletion time is automatically removed after 24 hours. The auto-deletion time can range from 0 seconds (immediate deletion) to 365 days. 12. Click **Continue**. 13. In the **Choose tables** field, click **Browse** . Choose one or more tables to scan, and then click **Select**. 14. Click **Continue**. 15. Optional: Export the scan results to a BigQuery standard table. In the **Export scan results to BigQuery table** section, do the following: 1. In the **Select BigQuery dataset** field, click **Browse**. Select a BigQuery dataset to store the data profile scan results. 2. In the **BigQuery table** field, specify the table to store the data profile scan results. If you're using an existing table, make sure that it's compatible with the [export table schema](https://docs.cloud.google.com/knowledge-catalog/docs/use-data-profiling.md.txt#table-schema). If the specified table doesn't exist, Knowledge Catalog creates it for you. Knowledge Catalog uses the same results table for all of the data profile scans. 16. Optional: Add labels. Labels are key-value pairs that let you group related objects together or with other Google Cloud resources. 17. To create the scans, click **Create**. If you set the schedule to **On-demand** , you can also run the scans now by clicking **Run scan**.
## Run a data profile scan ### Console 1. In the Google Cloud console, go to the Knowledge Catalog **Data profiling \& quality** page.
[Go to Data profiling \& quality](https://console.cloud.google.com/dataplex/govern/data-profiling-and-quality) 2. Click the data profile scan to run. 3. Click **Run now**. ### gcloud To run a data profile scan, use the [`gcloud dataplex datascans run` command](https://docs.cloud.google.com/sdk/gcloud/reference/dataplex/datascans/run): ``` gcloud dataplex datascans run DATASCAN \ --location=LOCATION ``` Replace the following variables: - `DATASCAN`: The ID of the data profile scan. - `LOCATION`: The Google Cloud region in which the data profile scan was created. ### C# ### C# Before trying this sample, follow the C# setup instructions in the [Knowledge Catalog quickstart using client libraries](https://docs.cloud.google.com/knowledge-catalog/docs/reference/libraries). For more information, see the [Knowledge Catalog C# API reference documentation](https://docs.cloud.google.com/dotnet/docs/reference/Google.Cloud.Dataplex.V1/latest). To authenticate to Knowledge Catalog, set up Application Default Credentials. For more information, see [Set up authentication for a local development environment](https://docs.cloud.google.com/docs/authentication/set-up-adc-local-dev-environment.md.txt). ```c# using Google.Cloud.Dataplex.V1; public sealed partial class GeneratedDataScanServiceClientSnippets { /// Snippet for RunDataScan /// /// This snippet has been automatically generated and should be regarded as a code template only. /// It will require modifications to work: /// - It may require correct/in-range values for request initialization. /// - It may require specifying regional endpoints when creating the service client as shown in /// https://cloud.google.com/dotnet/docs/reference/help/client-configuration#endpoint. /// public void RunDataScanRequestObject() { // Create client DataScanServiceClient dataScanServiceClient = DataScanServiceClient.Create(); // Initialize request argument(s) RunDataScanRequest request = new RunDataScanRequest { DataScanName = DataScanName.FromProjectLocationDataScan("[PROJECT]", "[LOCATION]", "[DATASCAN]"), }; // Make the request RunDataScanResponse response = dataScanServiceClient.RunDataScan(request); } } ``` ### Go ### Go Before trying this sample, follow the Go setup instructions in the [Knowledge Catalog quickstart using client libraries](https://docs.cloud.google.com/knowledge-catalog/docs/reference/libraries). For more information, see the [Knowledge Catalog Go API reference documentation](https://pkg.go.dev/cloud.google.com/go/dataplex). To authenticate to Knowledge Catalog, set up Application Default Credentials. For more information, see [Set up authentication for a local development environment](https://docs.cloud.google.com/docs/authentication/set-up-adc-local-dev-environment.md.txt). ```go //go:build examples package main import ( "context" dataplex "cloud.google.com/go/dataplex/apiv1" dataplexpb "cloud.google.com/go/dataplex/apiv1/dataplexpb" ) func main() { ctx := context.Background() // This snippet has been automatically generated and should be regarded as a code template only. // It will require modifications to work: // - It may require correct/in-range values for request initialization. // - It may require specifying regional endpoints when creating the service client as shown in: // https://pkg.go.dev/cloud.google.com/go#hdr-Client_Options c, err := dataplex.NewDataScanClient(ctx) if err != nil { // TODO: Handle error. } defer c.Close() req := &dataplexpb.RunDataScanRequest{ // TODO: Fill request struct fields. // See https://pkg.go.dev/cloud.google.com/go/dataplex/apiv1/dataplexpb#RunDataScanRequest. } resp, err := c.RunDataScan(ctx, req) if err != nil { // TODO: Handle error. } // TODO: Use resp. _ = resp } ``` ### Java ### Java Before trying this sample, follow the Java setup instructions in the [Knowledge Catalog quickstart using client libraries](https://docs.cloud.google.com/knowledge-catalog/docs/reference/libraries). For more information, see the [Knowledge Catalog Java API reference documentation](https://docs.cloud.google.com/java/docs/reference/google-cloud-dataplex/latest/overview). To authenticate to Knowledge Catalog, set up Application Default Credentials. For more information, see [Set up authentication for a local development environment](https://docs.cloud.google.com/docs/authentication/set-up-adc-local-dev-environment.md.txt). ```java import com.google.cloud.dataplex.v1.DataScanName; import com.google.cloud.dataplex.v1.DataScanServiceClient; import com.google.cloud.dataplex.v1.RunDataScanRequest; import com.google.cloud.dataplex.v1.RunDataScanResponse; public class SyncRunDataScan { public static void main(String[] args) throws Exception { syncRunDataScan(); } public static void syncRunDataScan() throws Exception { // This snippet has been automatically generated and should be regarded as a code template only. // It will require modifications to work: // - It may require correct/in-range values for request initialization. // - It may require specifying regional endpoints when creating the service client as shown in // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library try (DataScanServiceClient dataScanServiceClient = DataScanServiceClient.create()) { RunDataScanRequest request = RunDataScanRequest.newBuilder() .setName(DataScanName.of("[PROJECT]", "[LOCATION]", "[DATASCAN]").toString()) .build(); RunDataScanResponse response = dataScanServiceClient.runDataScan(request); } } } ``` ### Python ### Python Before trying this sample, follow the Python setup instructions in the [Knowledge Catalog quickstart using client libraries](https://docs.cloud.google.com/knowledge-catalog/docs/reference/libraries). For more information, see the [Knowledge Catalog Python API reference documentation](https://docs.cloud.google.com/python/docs/reference/dataplex/latest). To authenticate to Knowledge Catalog, set up Application Default Credentials. For more information, see [Set up authentication for a local development environment](https://docs.cloud.google.com/docs/authentication/set-up-adc-local-dev-environment.md.txt). ```python # This snippet has been automatically generated and should be regarded as a # code template only. # It will require modifications to work: # - It may require correct/in-range values for request initialization. # - It may require specifying regional endpoints when creating the service # client as shown in: # https://googleapis.dev/python/google-api-core/latest/client_options.html from google.cloud import dataplex_v1 def sample_run_data_scan(): # Create a client client = dataplex_v1.DataScanServiceClient() # Initialize request argument(s) request = dataplex_v1.RunDataScanRequest( name="name_value", ) # Make the request response = client.run_data_scan(request=request) # Handle the response print(response) ``` ### Ruby ### Ruby Before trying this sample, follow the Ruby setup instructions in the [Knowledge Catalog quickstart using client libraries](https://docs.cloud.google.com/knowledge-catalog/docs/reference/libraries). For more information, see the [Knowledge Catalog Ruby API reference documentation](https://docs.cloud.google.com/ruby/docs/reference/google-cloud-dataplex/latest). To authenticate to Knowledge Catalog, set up Application Default Credentials. For more information, see [Set up authentication for a local development environment](https://docs.cloud.google.com/docs/authentication/set-up-adc-local-dev-environment.md.txt). ```ruby require "google/cloud/dataplex/v1" ## # Snippet for the run_data_scan call in the DataScanService service # # This snippet has been automatically generated and should be regarded as a code # template only. It will require modifications to work: # - It may require correct/in-range values for request initialization. # - It may require specifying regional endpoints when creating the service # client as shown in https://cloud.google.com/ruby/docs/reference. # # This is an auto-generated example demonstrating basic usage of # Google::Cloud::Dataplex::V1::DataScanService::Client#run_data_scan. # def run_data_scan # Create a client object. The client can be reused for multiple calls. client = Google::Cloud::Dataplex::V1::DataScanService::Client.new # Create a request. To set request fields, pass in keyword arguments. request = Google::Cloud::Dataplex::V1::RunDataScanRequest.new # Call the run_data_scan method. result = client.run_data_scan request # The returned object is of type Google::Cloud::Dataplex::V1::RunDataScanResponse. p result end ``` ### REST To run a data profile scan, use the [`dataScans.run` method](https://docs.cloud.google.com/knowledge-catalog/docs/reference/rest/v1/projects.locations.dataScans/run.md.txt). ### Airflow To run a data profile scan using a Directed Acyclic Graph (DAG) in [Managed Service for Apache Airflow (Cloud Composer)](https://docs.cloud.google.com/composer/docs.md.txt), use the [`DataplexRunDataProfileScanOperator`](https://airflow.apache.org/docs/apache-airflow-providers-google/stable/_api/airflow/providers/google/cloud/operators/dataplex/index.html#airflow.providers.google.cloud.operators.dataplex.DataplexRunDataProfileScanOperator): ``` from datetime import datetime from airflow import DAG from airflow.providers.google.cloud.operators.dataplex import DataplexRunDataProfileScanOperator with DAG( "dataplex_data_profile_scan", start_date=datetime(2026, 1, 1), schedule_interval="@daily", catchup=False, ) as dag: run_profile_scan = DataplexRunDataProfileScanOperator( task_id="run_dataplex_profile_scan", project_id="PROJECT_ID", region="REGION", data_scan_id="DATASCAN_ID", ) ``` Replace the following variables: - `PROJECT_ID`: Your Google Cloud project ID. - `REGION`: The Google Cloud region in which the data profile scan was created. - `DATASCAN_ID`: The ID of your data profile scan. > [!NOTE] > **Note:** Run isn't supported for data profile scans that are on a one-time schedule.
## View data profile scan results ### Console 1. In the Google Cloud console, go to the Knowledge Catalog **Data profiling \& quality** page. [Go to Data profiling \& quality](https://console.cloud.google.com/dataplex/govern/data-profiling-and-quality)
2. Click the name of a data profile scan. - The **Overview** section displays information about the most recent jobs, including when the scan was run, the number of table records scanned, and the job status. - The **Data profile scan configuration** section displays details about the scan. 3. To see detailed information about a job, such as the scanned table's columns, statistics about the columns that were found in the scan, and the job logs, click the **Jobs history** tab. Then, click a job ID. > [!NOTE] > **Note:** If you exported the scan results to a BigQuery table, then you can also access the scan results from the table. ### gcloud To view the results of a data profile scan job, use the [`gcloud dataplex datascans jobs describe` command](https://docs.cloud.google.com/sdk/gcloud/reference/dataplex/datascans/jobs/describe): ``` gcloud dataplex datascans jobs describe JOB \ --location=LOCATION \ --datascan=DATASCAN \ --view=FULL ``` Replace the following variables: - `JOB`: The job ID of the data profile scan job. - `LOCATION`: The Google Cloud region in which the data profile scan was created. - `DATASCAN`: The ID of the data profile scan that the job belongs to. - `--view=FULL`: To see the scan job result, specify `FULL`. ### C# ### C# Before trying this sample, follow the C# setup instructions in the [Knowledge Catalog quickstart using client libraries](https://docs.cloud.google.com/knowledge-catalog/docs/reference/libraries). For more information, see the [Knowledge Catalog C# API reference documentation](https://docs.cloud.google.com/dotnet/docs/reference/Google.Cloud.Dataplex.V1/latest). To authenticate to Knowledge Catalog, set up Application Default Credentials. For more information, see [Set up authentication for a local development environment](https://docs.cloud.google.com/docs/authentication/set-up-adc-local-dev-environment.md.txt). ```c# using Google.Cloud.Dataplex.V1; public sealed partial class GeneratedDataScanServiceClientSnippets { /// Snippet for GetDataScan /// /// This snippet has been automatically generated and should be regarded as a code template only. /// It will require modifications to work: /// - It may require correct/in-range values for request initialization. /// - It may require specifying regional endpoints when creating the service client as shown in /// https://cloud.google.com/dotnet/docs/reference/help/client-configuration#endpoint. /// public void GetDataScanRequestObject() { // Create client DataScanServiceClient dataScanServiceClient = DataScanServiceClient.Create(); // Initialize request argument(s) GetDataScanRequest request = new GetDataScanRequest { DataScanName = DataScanName.FromProjectLocationDataScan("[PROJECT]", "[LOCATION]", "[DATASCAN]"), View = GetDataScanRequest.Types.DataScanView.Unspecified, }; // Make the request DataScan response = dataScanServiceClient.GetDataScan(request); } } ``` ### Go ### Go Before trying this sample, follow the Go setup instructions in the [Knowledge Catalog quickstart using client libraries](https://docs.cloud.google.com/knowledge-catalog/docs/reference/libraries). For more information, see the [Knowledge Catalog Go API reference documentation](https://pkg.go.dev/cloud.google.com/go/dataplex). To authenticate to Knowledge Catalog, set up Application Default Credentials. For more information, see [Set up authentication for a local development environment](https://docs.cloud.google.com/docs/authentication/set-up-adc-local-dev-environment.md.txt). ```go //go:build examples package main import ( "context" dataplex "cloud.google.com/go/dataplex/apiv1" dataplexpb "cloud.google.com/go/dataplex/apiv1/dataplexpb" ) func main() { ctx := context.Background() // This snippet has been automatically generated and should be regarded as a code template only. // It will require modifications to work: // - It may require correct/in-range values for request initialization. // - It may require specifying regional endpoints when creating the service client as shown in: // https://pkg.go.dev/cloud.google.com/go#hdr-Client_Options c, err := dataplex.NewDataScanClient(ctx) if err != nil { // TODO: Handle error. } defer c.Close() req := &dataplexpb.GetDataScanRequest{ // TODO: Fill request struct fields. // See https://pkg.go.dev/cloud.google.com/go/dataplex/apiv1/dataplexpb#GetDataScanRequest. } resp, err := c.GetDataScan(ctx, req) if err != nil { // TODO: Handle error. } // TODO: Use resp. _ = resp } ``` ### Java ### Java Before trying this sample, follow the Java setup instructions in the [Knowledge Catalog quickstart using client libraries](https://docs.cloud.google.com/knowledge-catalog/docs/reference/libraries). For more information, see the [Knowledge Catalog Java API reference documentation](https://docs.cloud.google.com/java/docs/reference/google-cloud-dataplex/latest/overview). To authenticate to Knowledge Catalog, set up Application Default Credentials. For more information, see [Set up authentication for a local development environment](https://docs.cloud.google.com/docs/authentication/set-up-adc-local-dev-environment.md.txt). ```java import com.google.cloud.dataplex.v1.DataScan; import com.google.cloud.dataplex.v1.DataScanName; import com.google.cloud.dataplex.v1.DataScanServiceClient; import com.google.cloud.dataplex.v1.GetDataScanRequest; public class SyncGetDataScan { public static void main(String[] args) throws Exception { syncGetDataScan(); } public static void syncGetDataScan() throws Exception { // This snippet has been automatically generated and should be regarded as a code template only. // It will require modifications to work: // - It may require correct/in-range values for request initialization. // - It may require specifying regional endpoints when creating the service client as shown in // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library try (DataScanServiceClient dataScanServiceClient = DataScanServiceClient.create()) { GetDataScanRequest request = GetDataScanRequest.newBuilder() .setName(DataScanName.of("[PROJECT]", "[LOCATION]", "[DATASCAN]").toString()) .build(); DataScan response = dataScanServiceClient.getDataScan(request); } } } ``` ### Python ### Python Before trying this sample, follow the Python setup instructions in the [Knowledge Catalog quickstart using client libraries](https://docs.cloud.google.com/knowledge-catalog/docs/reference/libraries). For more information, see the [Knowledge Catalog Python API reference documentation](https://docs.cloud.google.com/python/docs/reference/dataplex/latest). To authenticate to Knowledge Catalog, set up Application Default Credentials. For more information, see [Set up authentication for a local development environment](https://docs.cloud.google.com/docs/authentication/set-up-adc-local-dev-environment.md.txt). ```python # This snippet has been automatically generated and should be regarded as a # code template only. # It will require modifications to work: # - It may require correct/in-range values for request initialization. # - It may require specifying regional endpoints when creating the service # client as shown in: # https://googleapis.dev/python/google-api-core/latest/client_options.html from google.cloud import dataplex_v1 def sample_get_data_scan(): # Create a client client = dataplex_v1.DataScanServiceClient() # Initialize request argument(s) request = dataplex_v1.GetDataScanRequest( name="name_value", ) # Make the request response = client.get_data_scan(request=request) # Handle the response print(response) ``` ### Ruby ### Ruby Before trying this sample, follow the Ruby setup instructions in the [Knowledge Catalog quickstart using client libraries](https://docs.cloud.google.com/knowledge-catalog/docs/reference/libraries). For more information, see the [Knowledge Catalog Ruby API reference documentation](https://docs.cloud.google.com/ruby/docs/reference/google-cloud-dataplex/latest). To authenticate to Knowledge Catalog, set up Application Default Credentials. For more information, see [Set up authentication for a local development environment](https://docs.cloud.google.com/docs/authentication/set-up-adc-local-dev-environment.md.txt). ```ruby require "google/cloud/dataplex/v1" ## # Snippet for the get_data_scan call in the DataScanService service # # This snippet has been automatically generated and should be regarded as a code # template only. It will require modifications to work: # - It may require correct/in-range values for request initialization. # - It may require specifying regional endpoints when creating the service # client as shown in https://cloud.google.com/ruby/docs/reference. # # This is an auto-generated example demonstrating basic usage of # Google::Cloud::Dataplex::V1::DataScanService::Client#get_data_scan. # def get_data_scan # Create a client object. The client can be reused for multiple calls. client = Google::Cloud::Dataplex::V1::DataScanService::Client.new # Create a request. To set request fields, pass in keyword arguments. request = Google::Cloud::Dataplex::V1::GetDataScanRequest.new # Call the get_data_scan method. result = client.get_data_scan request # The returned object is of type Google::Cloud::Dataplex::V1::DataScan. p result end ``` ### REST To view the results of a data profile scan, use the [`dataScans.get` method](https://docs.cloud.google.com/knowledge-catalog/docs/reference/rest/v1/projects.locations.dataScans/get.md.txt).
### View published results If the data profile scan results are published to the BigQuery and Knowledge Catalog pages in the Google Cloud console, then you can see the latest scan results on the source table's **Data profile** tab. 1. In the Google Cloud console, go to the BigQuery page. [Go to BigQuery](https://console.cloud.google.com/bigquery) 2. In the left pane, click **Explorer**: ![Highlighted button for the Explorer pane.](https://docs.cloud.google.com/static/bigquery/images/explorer-tab.png) If you don't see the left pane, click **Expand left pane** to open the pane. 3. In the **Explorer** pane, click **Datasets**, and then click your dataset. 4. Click **Overview \> Tables**, and then select the table whose data profile scan results you want to see. 5. Click the **Data profile** tab. The latest published results are displayed. > [!NOTE] > **Note:** Published results might not be available if a scan is running for the first time. ### View the most recent data profile scan job ### Console 1. In the Google Cloud console, go to the Knowledge Catalog **Data profiling \& quality** page. [Go to Data profiling \& quality](https://console.cloud.google.com/dataplex/govern/data-profiling-and-quality)
2. Click the name of a data profile scan. 3. Click the **Latest job results** tab. The **Latest job results** tab, when there's at least one successfully completed run, provides information about the most recent job. It lists the scanned table's columns and statistics about the columns that were found in the scan. ### gcloud To view the most recent successful data profile scan, use the [`gcloud dataplex datascans describe` command](https://docs.cloud.google.com/sdk/gcloud/reference/dataplex/datascans/describe): ``` gcloud dataplex datascans describe DATASCAN \ --location=LOCATION \ --view=FULL ``` Replace the following variables: - `DATASCAN`: The ID of the data profile scan to view the most recent job for. - `LOCATION`: The Google Cloud region in which the data profile scan was created. - `--view=FULL`: To see the scan job result, specify `FULL`. ### REST To view the most recent scan job, use the [`dataScans.get` method](https://docs.cloud.google.com/knowledge-catalog/docs/reference/rest/v1/projects.locations.dataScans/get.md.txt).
### View historical scan results Knowledge Catalog saves the data profile scan history of the last 300 jobs or for the past year, whichever occurs first. ### Console 1. In the Google Cloud console, go to the Knowledge Catalog **Data profiling \& quality** page. [Go to Data profiling \& quality](https://console.cloud.google.com/dataplex/govern/data-profiling-and-quality)
2. Click the name of a data profile scan. 3. Click the **Jobs history** tab. The **Jobs history** tab provides information about past jobs, such as the number of records scanned in each job, the job status, and the time the job was run. 4. To view detailed information about a job, click any of the jobs in the **Job ID** column. ### gcloud To view historical data profile scan jobs, use the [`gcloud dataplex datascans jobs list` command](https://docs.cloud.google.com/sdk/gcloud/reference/dataplex/datascans/jobs/list): ``` gcloud dataplex datascans jobs list \ --location=LOCATION \ --datascan=DATASCAN ``` Replace the following variables: - `LOCATION`: The Google Cloud region in which the data profile scan was created. - `DATASCAN`: The ID of the data profile scan to view jobs for. ### C# ### C# Before trying this sample, follow the C# setup instructions in the [Knowledge Catalog quickstart using client libraries](https://docs.cloud.google.com/knowledge-catalog/docs/reference/libraries). For more information, see the [Knowledge Catalog C# API reference documentation](https://docs.cloud.google.com/dotnet/docs/reference/Google.Cloud.Dataplex.V1/latest). To authenticate to Knowledge Catalog, set up Application Default Credentials. For more information, see [Set up authentication for a local development environment](https://docs.cloud.google.com/docs/authentication/set-up-adc-local-dev-environment.md.txt). ```c# using Google.Api.Gax; using Google.Cloud.Dataplex.V1; using System; public sealed partial class GeneratedDataScanServiceClientSnippets { /// Snippet for ListDataScanJobs /// /// This snippet has been automatically generated and should be regarded as a code template only. /// It will require modifications to work: /// - It may require correct/in-range values for request initialization. /// - It may require specifying regional endpoints when creating the service client as shown in /// https://cloud.google.com/dotnet/docs/reference/help/client-configuration#endpoint. /// public void ListDataScanJobsRequestObject() { // Create client DataScanServiceClient dataScanServiceClient = DataScanServiceClient.Create(); // Initialize request argument(s) ListDataScanJobsRequest request = new ListDataScanJobsRequest { ParentAsDataScanName = DataScanName.FromProjectLocationDataScan("[PROJECT]", "[LOCATION]", "[DATASCAN]"), Filter = "", }; // Make the request PagedEnumerable response = dataScanServiceClient.ListDataScanJobs(request); // Iterate over all response items, lazily performing RPCs as required foreach (DataScanJob item in response) { // Do something with each item Console.WriteLine(item); } // Or iterate over pages (of server-defined size), performing one RPC per page foreach (ListDataScanJobsResponse page in response.AsRawResponses()) { // Do something with each page of items Console.WriteLine("A page of results:"); foreach (DataScanJob item in page) { // Do something with each item Console.WriteLine(item); } } // Or retrieve a single page of known size (unless it's the final page), performing as many RPCs as required int pageSize = 10; Page singlePage = response.ReadPage(pageSize); // Do something with the page of items Console.WriteLine($"A page of {pageSize} results (unless it's the final page):"); foreach (DataScanJob item in singlePage) { // Do something with each item Console.WriteLine(item); } // Store the pageToken, for when the next page is required. string nextPageToken = singlePage.NextPageToken; } } ``` ### Go ### Go Before trying this sample, follow the Go setup instructions in the [Knowledge Catalog quickstart using client libraries](https://docs.cloud.google.com/knowledge-catalog/docs/reference/libraries). For more information, see the [Knowledge Catalog Go API reference documentation](https://pkg.go.dev/cloud.google.com/go/dataplex). To authenticate to Knowledge Catalog, set up Application Default Credentials. For more information, see [Set up authentication for a local development environment](https://docs.cloud.google.com/docs/authentication/set-up-adc-local-dev-environment.md.txt). ```go //go:build examples package main import ( "context" dataplex "cloud.google.com/go/dataplex/apiv1" dataplexpb "cloud.google.com/go/dataplex/apiv1/dataplexpb" "google.golang.org/api/iterator" ) func main() { ctx := context.Background() // This snippet has been automatically generated and should be regarded as a code template only. // It will require modifications to work: // - It may require correct/in-range values for request initialization. // - It may require specifying regional endpoints when creating the service client as shown in: // https://pkg.go.dev/cloud.google.com/go#hdr-Client_Options c, err := dataplex.NewDataScanClient(ctx) if err != nil { // TODO: Handle error. } defer c.Close() req := &dataplexpb.ListDataScanJobsRequest{ // TODO: Fill request struct fields. // See https://pkg.go.dev/cloud.google.com/go/dataplex/apiv1/dataplexpb#ListDataScanJobsRequest. } it := c.ListDataScanJobs(ctx, req) for { resp, err := it.Next() if err == iterator.Done { break } if err != nil { // TODO: Handle error. } // TODO: Use resp. _ = resp // If you need to access the underlying RPC response, // you can do so by casting the `Response` as below. // Otherwise, remove this line. Only populated after // first call to Next(). Not safe for concurrent access. _ = it.Response.(*dataplexpb.ListDataScanJobsResponse) } } ``` ### Java ### Java Before trying this sample, follow the Java setup instructions in the [Knowledge Catalog quickstart using client libraries](https://docs.cloud.google.com/knowledge-catalog/docs/reference/libraries). For more information, see the [Knowledge Catalog Java API reference documentation](https://docs.cloud.google.com/java/docs/reference/google-cloud-dataplex/latest/overview). To authenticate to Knowledge Catalog, set up Application Default Credentials. For more information, see [Set up authentication for a local development environment](https://docs.cloud.google.com/docs/authentication/set-up-adc-local-dev-environment.md.txt). ```java import com.google.cloud.dataplex.v1.DataScanJob; import com.google.cloud.dataplex.v1.DataScanName; import com.google.cloud.dataplex.v1.DataScanServiceClient; import com.google.cloud.dataplex.v1.ListDataScanJobsRequest; public class SyncListDataScanJobs { public static void main(String[] args) throws Exception { syncListDataScanJobs(); } public static void syncListDataScanJobs() throws Exception { // This snippet has been automatically generated and should be regarded as a code template only. // It will require modifications to work: // - It may require correct/in-range values for request initialization. // - It may require specifying regional endpoints when creating the service client as shown in // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library try (DataScanServiceClient dataScanServiceClient = DataScanServiceClient.create()) { ListDataScanJobsRequest request = ListDataScanJobsRequest.newBuilder() .setParent(DataScanName.of("[PROJECT]", "[LOCATION]", "[DATASCAN]").toString()) .setPageSize(883849137) .setPageToken("pageToken873572522") .setFilter("filter-1274492040") .build(); for (DataScanJob element : dataScanServiceClient.listDataScanJobs(request).iterateAll()) { // doThingsWith(element); } } } } ``` ### Python ### Python Before trying this sample, follow the Python setup instructions in the [Knowledge Catalog quickstart using client libraries](https://docs.cloud.google.com/knowledge-catalog/docs/reference/libraries). For more information, see the [Knowledge Catalog Python API reference documentation](https://docs.cloud.google.com/python/docs/reference/dataplex/latest). To authenticate to Knowledge Catalog, set up Application Default Credentials. For more information, see [Set up authentication for a local development environment](https://docs.cloud.google.com/docs/authentication/set-up-adc-local-dev-environment.md.txt). ```python # This snippet has been automatically generated and should be regarded as a # code template only. # It will require modifications to work: # - It may require correct/in-range values for request initialization. # - It may require specifying regional endpoints when creating the service # client as shown in: # https://googleapis.dev/python/google-api-core/latest/client_options.html from google.cloud import dataplex_v1 def sample_list_data_scan_jobs(): # Create a client client = dataplex_v1.DataScanServiceClient() # Initialize request argument(s) request = dataplex_v1.ListDataScanJobsRequest( parent="parent_value", ) # Make the request page_result = client.list_data_scan_jobs(request=request) # Handle the response for response in page_result: print(response) ``` ### Ruby ### Ruby Before trying this sample, follow the Ruby setup instructions in the [Knowledge Catalog quickstart using client libraries](https://docs.cloud.google.com/knowledge-catalog/docs/reference/libraries). For more information, see the [Knowledge Catalog Ruby API reference documentation](https://docs.cloud.google.com/ruby/docs/reference/google-cloud-dataplex/latest). To authenticate to Knowledge Catalog, set up Application Default Credentials. For more information, see [Set up authentication for a local development environment](https://docs.cloud.google.com/docs/authentication/set-up-adc-local-dev-environment.md.txt). ```ruby require "google/cloud/dataplex/v1" ## # Snippet for the list_data_scan_jobs call in the DataScanService service # # This snippet has been automatically generated and should be regarded as a code # template only. It will require modifications to work: # - It may require correct/in-range values for request initialization. # - It may require specifying regional endpoints when creating the service # client as shown in https://cloud.google.com/ruby/docs/reference. # # This is an auto-generated example demonstrating basic usage of # Google::Cloud::Dataplex::V1::DataScanService::Client#list_data_scan_jobs. # def list_data_scan_jobs # Create a client object. The client can be reused for multiple calls. client = Google::Cloud::Dataplex::V1::DataScanService::Client.new # Create a request. To set request fields, pass in keyword arguments. request = Google::Cloud::Dataplex::V1::ListDataScanJobsRequest.new # Call the list_data_scan_jobs method. result = client.list_data_scan_jobs request # The returned object is of type Gapic::PagedEnumerable. You can iterate # over elements, and API calls will be issued to fetch pages as needed. result.each do |item| # Each element is of type ::Google::Cloud::Dataplex::V1::DataScanJob. p item end end ``` ### REST To view historical data profile scan jobs, use the [`dataScans.jobs.list` method](https://docs.cloud.google.com/knowledge-catalog/docs/reference/rest/v1/projects.locations.dataScans.jobs/list.md.txt). ### Job statuses Data scan jobs can have the following statuses: - **`PENDING`**: the job is created but has not started running. In this state, the scan actively sets up infrastructure or acquires slots. This phase can take 10 to 20 seconds or more, depending on schema complexity and resource contention. - **`RUNNING`**: the job is running. - **`CANCELING`**: the job is canceling. - **`CANCELLED`**: the job is successfully canceled. - **`SUCCEEDED`**: the job completes successfully. The completion status might lag the actual query completion by up to 60 seconds because of post-processing steps, such as aggregation and metadata synchronization. - **`FAILED`**: the job fails because of an error. The completion status might lag the actual query completion by up to 60 seconds because of post-processing steps, such as aggregation and metadata synchronization. - **`SUCCEEDED_WITH_ERRORS`**: the job completes successfully, but some errors occur during execution. #### Monitoring best practices When you monitor your data scan jobs, keep the following best practices in mind: - **Avoid aggressive polling** : avoid aggressive polling (for example, calling `GetJob` or equivalent every 1 to 5 seconds). Aggressive polling wastes API quota and doesn't accelerate state transitions. - **Use exponential backoff**: if repetitive polling is unavoidable, use exponential backoff starting at 10 to 15 seconds. - **Use asynchronous event-driven decoupling (Recommended)** : instead of synchronous API polling, use Eventarc triggers that listen to Cloud Audit Logs (`cloudaudit.googleapis.com`). You can use these triggers to initiate downstream workflows immediately upon DataScan job completion events. This approach bypasses API polling limits, conserves quota, and reduces perceived latency.
### View the data profile scans for a table To view all data profile scans that apply to a specific table, do the following: 1. In the Google Cloud console, go to the **BigQuery Studio** page. [Go to BigQuery Studio](https://console.cloud.google.com/bigquery) 2. In the **Explorer** pane, click **Datasets**, and then click your dataset. 3. Click **Overview \> Tables**, and then select the table whose data profile scans you want to see. 4. Click the **Data profile** tab. 5. Click **Data profile scan** , and then select **View all scans** . This opens the **Data profiling \& quality** page in Knowledge Catalog, filtered by your table. ## Grant access to data profile scan results To let users in your organization view the scan results, do the following: 1. In the Google Cloud console, go to the Knowledge Catalog **Data profiling \& quality** page. [Go to Data profiling \& quality](https://console.cloud.google.com/dataplex/govern/data-profiling-and-quality)
2. Click the data profile scan you want to share the results of. 3. Click the **Permissions** tab. 4. Do the following:
- To grant access to a principal, click **Grant access** . Grant the **Dataplex DataScan DataViewer** role to the associated principal. - To remove access from a principal, select the principal that you want to remove the **Dataplex DataScan DataViewer** role from. Click **Remove access**, and then confirm when prompted.
## Manage data profile scans for a specific table You can create and manage data profile scans directly when working with a specific table. In the Google Cloud console, on the BigQuery page for the table, use the **Data profile** tab. Do the following: 1. In the Google Cloud console, go to the **BigQuery** page. [Go to BigQuery](https://console.cloud.google.com/bigquery)
In the **Explorer** pane, click **Datasets** , and then click your dataset. Click **Overview \> Tables**, and select the table whose data profile scan results you want to see. 2. Click the **Data profile** tab. 3. Depending on whether the table has a data profile scan whose results are published, you can work with the table's data profile scans in the following ways: - **Data profile scan results are published**: the latest published scan results are displayed on the page. To manage the data profile scans for this table, click **Data profile scan**, and then select from the following options: - **Create new scan** : create a new data profile scan. For more information, see the [Create a data profile scan](https://docs.cloud.google.com/bigquery/docs/data-profile-scan.md.txt#create-scan) section of this document. When you create a scan from a table's details page, the table is preselected. - **Run now**: run the scan. - **Edit scan configuration**: edit settings including the display name, filters, sampling size, and schedule. - **Manage scan permissions** : control who can access the scan results. For more information, see the [Grant access to data profile scan results](https://docs.cloud.google.com/bigquery/docs/data-profile-scan.md.txt#share-results) section of this document. - **View historical results** : view detailed information about previous data profile scan jobs. For more information, see the [View data profile scan results](https://docs.cloud.google.com/bigquery/docs/data-profile-scan.md.txt#results) and [View historical scan results](https://docs.cloud.google.com/bigquery/docs/data-profile-scan.md.txt#older-scans) sections of this document. - **View all scans**: view a list of data profile scans that apply to this table. - **Data profile scan results aren't published** : click the menu next to **Quick data profile**, and then select from the following options: - **Customize data profiling** : create a new data profile scan. For more information, see the [Create a data profile scan](https://docs.cloud.google.com/bigquery/docs/data-profile-scan.md.txt#create-scan) section of this document. When you create a scan from a table's details page, the table's preselected. - **View previous profiles**: view a list of data profile scans that apply to this table.
## Update a data profile scan ### Console 1. In the Google Cloud console, go to the Knowledge Catalog **Data profiling \& quality** page. [Go to Data profiling \& quality](https://console.cloud.google.com/dataplex/govern/data-profiling-and-quality)
2. Click the name of a data profile scan. 3. Click **Edit**, and then edit the values. 4. Click **Save**. ### gcloud To update a data profile scan, use the [`gcloud dataplex datascans update data-profile` command](https://docs.cloud.google.com/sdk/gcloud/reference/dataplex/datascans/update/data-profile): ``` gcloud dataplex datascans update data-profile DATASCAN \ --location=LOCATION \ --description=DESCRIPTION ``` Replace the following variables: - `DATASCAN`: The ID of the data profile scan to update. - `LOCATION`: The Google Cloud region in which the data profile scan was created. - `DESCRIPTION`: The new description for the data profile scan. > [!NOTE] > **Note:** You can update specification fields, such as `rowFilter`, `samplingPercent`, or `includeFields`, in the data profile specification file. See the [JSON format](https://docs.cloud.google.com/knowledge-catalog/docs/reference/rest/v1/DataProfileSpec.md.txt). ### C# ### C# Before trying this sample, follow the C# setup instructions in the [Knowledge Catalog quickstart using client libraries](https://docs.cloud.google.com/knowledge-catalog/docs/reference/libraries). For more information, see the [Knowledge Catalog C# API reference documentation](https://docs.cloud.google.com/dotnet/docs/reference/Google.Cloud.Dataplex.V1/latest). To authenticate to Knowledge Catalog, set up Application Default Credentials. For more information, see [Set up authentication for a local development environment](https://docs.cloud.google.com/docs/authentication/set-up-adc-local-dev-environment.md.txt). ```c# using Google.Cloud.Dataplex.V1; using Google.LongRunning; using Google.Protobuf.WellKnownTypes; public sealed partial class GeneratedDataScanServiceClientSnippets { /// Snippet for UpdateDataScan /// /// This snippet has been automatically generated and should be regarded as a code template only. /// It will require modifications to work: /// - It may require correct/in-range values for request initialization. /// - It may require specifying regional endpoints when creating the service client as shown in /// https://cloud.google.com/dotnet/docs/reference/help/client-configuration#endpoint. /// public void UpdateDataScanRequestObject() { // Create client DataScanServiceClient dataScanServiceClient = DataScanServiceClient.Create(); // Initialize request argument(s) UpdateDataScanRequest request = new UpdateDataScanRequest { DataScan = new DataScan(), UpdateMask = new FieldMask(), ValidateOnly = false, }; // Make the request Operation response = dataScanServiceClient.UpdateDataScan(request); // Poll until the returned long-running operation is complete Operation completedResponse = response.PollUntilCompleted(); // Retrieve the operation result DataScan result = completedResponse.Result; // Or get the name of the operation string operationName = response.Name; // This name can be stored, then the long-running operation retrieved later by name Operation retrievedResponse = dataScanServiceClient.PollOnceUpdateDataScan(operationName); // Check if the retrieved long-running operation has completed if (retrievedResponse.IsCompleted) { // If it has completed, then access the result DataScan retrievedResult = retrievedResponse.Result; } } } ``` ### Go ### Go Before trying this sample, follow the Go setup instructions in the [Knowledge Catalog quickstart using client libraries](https://docs.cloud.google.com/knowledge-catalog/docs/reference/libraries). For more information, see the [Knowledge Catalog Go API reference documentation](https://pkg.go.dev/cloud.google.com/go/dataplex). To authenticate to Knowledge Catalog, set up Application Default Credentials. For more information, see [Set up authentication for a local development environment](https://docs.cloud.google.com/docs/authentication/set-up-adc-local-dev-environment.md.txt). ```go //go:build examples package main import ( "context" dataplex "cloud.google.com/go/dataplex/apiv1" dataplexpb "cloud.google.com/go/dataplex/apiv1/dataplexpb" ) func main() { ctx := context.Background() // This snippet has been automatically generated and should be regarded as a code template only. // It will require modifications to work: // - It may require correct/in-range values for request initialization. // - It may require specifying regional endpoints when creating the service client as shown in: // https://pkg.go.dev/cloud.google.com/go#hdr-Client_Options c, err := dataplex.NewDataScanClient(ctx) if err != nil { // TODO: Handle error. } defer c.Close() req := &dataplexpb.UpdateDataScanRequest{ // TODO: Fill request struct fields. // See https://pkg.go.dev/cloud.google.com/go/dataplex/apiv1/dataplexpb#UpdateDataScanRequest. } op, err := c.UpdateDataScan(ctx, req) if err != nil { // TODO: Handle error. } resp, err := op.Wait(ctx) if err != nil { // TODO: Handle error. } // TODO: Use resp. _ = resp } ``` ### Java ### Java Before trying this sample, follow the Java setup instructions in the [Knowledge Catalog quickstart using client libraries](https://docs.cloud.google.com/knowledge-catalog/docs/reference/libraries). For more information, see the [Knowledge Catalog Java API reference documentation](https://docs.cloud.google.com/java/docs/reference/google-cloud-dataplex/latest/overview). To authenticate to Knowledge Catalog, set up Application Default Credentials. For more information, see [Set up authentication for a local development environment](https://docs.cloud.google.com/docs/authentication/set-up-adc-local-dev-environment.md.txt). ```java import com.google.cloud.dataplex.v1.DataScan; import com.google.cloud.dataplex.v1.DataScanServiceClient; import com.google.cloud.dataplex.v1.UpdateDataScanRequest; import com.google.protobuf.FieldMask; public class SyncUpdateDataScan { public static void main(String[] args) throws Exception { syncUpdateDataScan(); } public static void syncUpdateDataScan() throws Exception { // This snippet has been automatically generated and should be regarded as a code template only. // It will require modifications to work: // - It may require correct/in-range values for request initialization. // - It may require specifying regional endpoints when creating the service client as shown in // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library try (DataScanServiceClient dataScanServiceClient = DataScanServiceClient.create()) { UpdateDataScanRequest request = UpdateDataScanRequest.newBuilder() .setDataScan(DataScan.newBuilder().build()) .setUpdateMask(FieldMask.newBuilder().build()) .setValidateOnly(true) .build(); DataScan response = dataScanServiceClient.updateDataScanAsync(request).get(); } } } ``` ### Python ### Python Before trying this sample, follow the Python setup instructions in the [Knowledge Catalog quickstart using client libraries](https://docs.cloud.google.com/knowledge-catalog/docs/reference/libraries). For more information, see the [Knowledge Catalog Python API reference documentation](https://docs.cloud.google.com/python/docs/reference/dataplex/latest). To authenticate to Knowledge Catalog, set up Application Default Credentials. For more information, see [Set up authentication for a local development environment](https://docs.cloud.google.com/docs/authentication/set-up-adc-local-dev-environment.md.txt). ```python # This snippet has been automatically generated and should be regarded as a # code template only. # It will require modifications to work: # - It may require correct/in-range values for request initialization. # - It may require specifying regional endpoints when creating the service # client as shown in: # https://googleapis.dev/python/google-api-core/latest/client_options.html from google.cloud import dataplex_v1 def sample_update_data_scan(): # Create a client client = dataplex_v1.DataScanServiceClient() # Initialize request argument(s) data_scan = dataplex_v1.DataScan() data_scan.data.entity = "entity_value" request = dataplex_v1.UpdateDataScanRequest( data_scan=data_scan, ) # Make the request operation = client.update_data_scan(request=request) print("Waiting for operation to complete...") response = operation.result() # Handle the response print(response) ``` ### Ruby ### Ruby Before trying this sample, follow the Ruby setup instructions in the [Knowledge Catalog quickstart using client libraries](https://docs.cloud.google.com/knowledge-catalog/docs/reference/libraries). For more information, see the [Knowledge Catalog Ruby API reference documentation](https://docs.cloud.google.com/ruby/docs/reference/google-cloud-dataplex/latest). To authenticate to Knowledge Catalog, set up Application Default Credentials. For more information, see [Set up authentication for a local development environment](https://docs.cloud.google.com/docs/authentication/set-up-adc-local-dev-environment.md.txt). ```ruby require "google/cloud/dataplex/v1" ## # Snippet for the update_data_scan call in the DataScanService service # # This snippet has been automatically generated and should be regarded as a code # template only. It will require modifications to work: # - It may require correct/in-range values for request initialization. # - It may require specifying regional endpoints when creating the service # client as shown in https://cloud.google.com/ruby/docs/reference. # # This is an auto-generated example demonstrating basic usage of # Google::Cloud::Dataplex::V1::DataScanService::Client#update_data_scan. # def update_data_scan # Create a client object. The client can be reused for multiple calls. client = Google::Cloud::Dataplex::V1::DataScanService::Client.new # Create a request. To set request fields, pass in keyword arguments. request = Google::Cloud::Dataplex::V1::UpdateDataScanRequest.new # Call the update_data_scan method. result = client.update_data_scan request # The returned object is of type Gapic::Operation. You can use it to # check the status of an operation, cancel it, or wait for results. # Here is how to wait for a response. result.wait_until_done! timeout: 60 if result.response? p result.response else puts "No response received." end end ``` ### REST To edit a data profile scan, use the [`dataScans.patch` method](https://docs.cloud.google.com/knowledge-catalog/docs/reference/rest/v1/projects.locations.dataScans/patch.md.txt). > [!NOTE] > **Note:** Update isn't supported for data profile scans that are on a one-time schedule.
## Delete a data profile scan ### Console 1. In the Google Cloud console, go to the Knowledge Catalog **Data profiling \& quality** page. [Go to Data profiling \& quality](https://console.cloud.google.com/dataplex/govern/data-profiling-and-quality)
2. Click the scan you want to delete. 3. Click **Delete**, and then confirm when prompted. ### gcloud To delete a data profile scan, use the [`gcloud dataplex datascans delete` command](https://docs.cloud.google.com/sdk/gcloud/reference/dataplex/datascans/delete): ``` gcloud dataplex datascans delete DATASCAN \ --location=LOCATION --async ``` Replace the following variables: - `DATASCAN`: The ID of the data profile scan to delete. - `LOCATION`: The Google Cloud region in which the data profile scan was created. ### REST To delete a data profile scan, use the [`dataScans.delete` method](https://docs.cloud.google.com/knowledge-catalog/docs/reference/rest/v1/projects.locations.dataScans/delete.md.txt). > [!NOTE] > **Note:** Delete isn't supported for data profile scans that are on a one-time schedule.
## What's next - Learn how to [explore your data by generating data insights](https://docs.cloud.google.com/bigquery/docs/data-insights.md.txt). - Learn more about [data governance in BigQuery](https://docs.cloud.google.com/bigquery/docs/data-governance.md.txt). - Learn how to [scan your data for data quality issues](https://docs.cloud.google.com/bigquery/docs/data-quality-scan.md.txt). - Learn how to preview schema details and construct queries with the [Reference panel](https://docs.cloud.google.com/bigquery/docs/running-queries.md.txt#use-reference-panel).