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You can update a feature group to register a BigQuery table or view
as the feature data source for that feature group. If the feature group already has
an associated data source, you can associate a different BigQuery
table or view as the feature data source.
While creating or updating a feature group, you have the option to add user-defined
metadata in the form of labels to the feature group. For more information about
how to update user-defined labels for a feature group, seeUpdate labels for a feature group.
Before you begin
Authenticate
to
Vertex AI, unless you've done so already.
To use the REST API samples on this page in a local development environment, you use the
credentials you provide to the gcloud CLI.
Installthe Google Cloud CLI.
After installation,initializethe Google Cloud CLI by running the following command:
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-09-04 UTC."],[],[],null,["# Update a feature group\n\n| **Preview**\n|\n|\n| This feature is subject to the \"Pre-GA Offerings Terms\" in the General Service Terms section\n| of the [Service Specific Terms](/terms/service-terms#1).\n|\n| Pre-GA features are available \"as is\" and might have limited support.\n|\n| For more information, see the\n| [launch stage descriptions](/products#product-launch-stages).\n\nYou can update a feature group to register a BigQuery table or view\nas the feature data source for that feature group. If the feature group already has\nan associated data source, you can associate a different BigQuery\ntable or view as the feature data source.\n| **Caution:** You can update a feature group even if there are feature views and features associated with it. If the updated data source excludes a column that's being used for online serving or is associated with a feature view or feature, then you also need to update that [feature view](/vertex-ai/docs/featurestore/latest/update-featureview) or [feature](/vertex-ai/docs/featurestore/latest/update-feature).\n\nWhile creating or updating a feature group, you have the option to add user-defined\nmetadata in the form of labels to the feature group. For more information about\nhow to update user-defined labels for a feature group, see\n[Update labels for a feature group](/vertex-ai/docs/featurestore/latest/feature-labels#feature_group).\n\nBefore you begin\n----------------\n\n\nto\nVertex AI, unless you've done so already.\n\n\nTo use the REST API samples on this page in a local development environment, you use the\ncredentials you provide to the gcloud CLI.\n\n1. [Install](/sdk/docs/install) the Google Cloud CLI. After installation, [initialize](/sdk/docs/initializing) the Google Cloud CLI by running the following command: \n\n```bash\ngcloud init\n```\n2. If you're using an external identity provider (IdP), you must first [sign in to the gcloud CLI with your federated identity](/iam/docs/workforce-log-in-gcloud).\n\n\nFor more information, see\n[Authenticate for using REST](/docs/authentication/rest)\nin the Google Cloud authentication documentation.\n\nUpdate a feature group\n----------------------\n\nUse the following sample to update a feature group. \n\n### REST\n\n\nTo update a [`FeatureGroup`](/vertex-ai/docs/reference/rest/v1/projects.locations.featureGroups#resource:-featuregroup)\nresource, send a `PATCH` request by using the\n[featureGroups.patch](/vertex-ai/docs/reference/rest/v1/projects.locations.featureGroups/patch)\nmethod.\n\n\nBefore using any of the request data,\nmake the following replacements:\n\n- \u003cvar translate=\"no\"\u003eLOCATION_ID\u003c/var\u003e: Region where the feature group is located, such as `us-central1`.\n- \u003cvar translate=\"no\"\u003ePROJECT_ID\u003c/var\u003e: Your project ID.\n- \u003cvar translate=\"no\"\u003eFEATURE_GROUP_NAME\u003c/var\u003e: The name of the feature group that you want to update.\n- \u003cvar translate=\"no\"\u003eENTITY_ID_COLUMNS\u003c/var\u003e: The names of the column(s) containing the entity IDs. You can specify either one column or multiple columns.\n - To specify only one entity ID column, specify the column name in the following format: \n `\"entity_id_column_name\"`.\n - To specify multiple entity ID columns, specify the column names in the following format: \n `[\"entity_id_column_1_name\", \"entity_id_column_2_name\", ...]`.\n- \u003cvar translate=\"no\"\u003eBIGQUERY_SOURCE_URI\u003c/var\u003e: URI of the BigQuery source table or view that you want to associate with the feature group.\n\n\nHTTP method and URL:\n\n```\nPATCH https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featureGroups?feature_group_id=FEATURE_GROUP_NAME\n```\n\n\nRequest JSON body:\n\n```\n{\n \"big_query\": {\n \"entity_id_columns\": \"ENTITY_ID_COLUMNS\",\n \"big_query_source\": {\n \"input_uri\": \"BIGQUERY_SOURCE_URI\"\n }\n }\n}\n```\n\nTo send your request, choose one of these options: \n\n#### curl\n\n| **Note:** The following command assumes that you have logged in to the `gcloud` CLI with your user account by running [`gcloud init`](/sdk/gcloud/reference/init) or [`gcloud auth login`](/sdk/gcloud/reference/auth/login) , or by using [Cloud Shell](/shell/docs), which automatically logs you into the `gcloud` CLI . You can check the currently active account by running [`gcloud auth list`](/sdk/gcloud/reference/auth/list).\n\n\nSave the request body in a file named `request.json`,\nand execute the following command:\n\n```\ncurl -X PATCH \\\n -H \"Authorization: Bearer $(gcloud auth print-access-token)\" \\\n -H \"Content-Type: application/json; charset=utf-8\" \\\n -d @request.json \\\n \"https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featureGroups?feature_group_id=FEATURE_GROUP_NAME\"\n```\n\n#### PowerShell\n\n| **Note:** The following command assumes that you have logged in to the `gcloud` CLI with your user account by running [`gcloud init`](/sdk/gcloud/reference/init) or [`gcloud auth login`](/sdk/gcloud/reference/auth/login) . You can check the currently active account by running [`gcloud auth list`](/sdk/gcloud/reference/auth/list).\n\n\nSave the request body in a file named `request.json`,\nand execute the following command:\n\n```\n$cred = gcloud auth print-access-token\n$headers = @{ \"Authorization\" = \"Bearer $cred\" }\n\nInvoke-WebRequest `\n -Method PATCH `\n -Headers $headers `\n -ContentType: \"application/json; charset=utf-8\" `\n -InFile request.json `\n -Uri \"https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featureGroups?feature_group_id=FEATURE_GROUP_NAME\" | Select-Object -Expand Content\n```\n\nYou should receive a JSON response similar to the following:\n\n```\n{\n \"name\": \"projects/PROJECT_NUMBER/locations/LOCATION_ID/featureGroups/FEATURE_GROUP_NAME/operations/OPERATION_ID\",\n \"metadata\": {\n \"@type\": \"type.googleapis.com/google.cloud.aiplatform.v1.UpdateFeatureGroupOperationMetadata\",\n \"genericMetadata\": {\n \"createTime\": \"2023-09-18T03:00:13.060636Z\",\n \"updateTime\": \"2023-09-18T03:00:13.060636Z\"\n }\n },\n \"done\": true,\n \"response\": {\n \"@type\": \"type.googleapis.com/google.cloud.aiplatform.v1.FeatureGroup\",\n \"name\": \"projects/PROJECT_NUMBER/locations/LOCATION_ID/featureGroups/FEATURE_GROUP_NAME\"\n }\n}\n```\n\n\u003cbr /\u003e\n\nWhat's next\n-----------\n\n- Learn how to [update a feature view](/vertex-ai/docs/featurestore/latest/update-featureview).\n\n- Learn how to [update a feature](/vertex-ai/docs/featurestore/latest/update-feature).\n\n- Learn how to [delete a feature group](/vertex-ai/docs/featurestore/latest/update-featureview)."]]
