BigQuery supports clustering for both partitioned and non-partitioned tables.

Reference: [https://cloud.google.com/bigquery/docs/clustered-tables](https://cloud.google.com/bigquery/docs/clustered-tables)
airflow/providers/google/cloud/hooks/bigquery.py
**Example**: ::
                schema=[{"name": "emp_name", "type": "STRING", "mode": "REQUIRED"},
                               {"name": "salary", "type": "INTEGER", "mode": "NULLABLE"}]
        :type schema: list
        :param time_partitioning: [Optional] A dictionary containing time-based partitioning
             definition for the table.
        :type time_partitioning: dict
        :param view: [Optional] A dictionary containing definition for the view.
            If set, it will patch a view instead of a table:
            https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#ViewDefinition
            **Example**: ::
                view = {
                    "query": "SELECT * FROM `test-project-id.test_dataset_id.test_table_prefix*` LIMIT 500",
                    "useLegacySql": False
                }
        :type view: dict
        :param require_partition_filter: [Optional] If true, queries over the this table require a
            partition filter. If false, queries over the table
        :type require_partition_filter: bool
        :param encryption_configuration: [Optional] Custom encryption configuration (e.g., Cloud KMS keys).
            **Example**: ::
                encryption_configuration = {
                    "kmsKeyName": "projects/testp/locations/us/keyRings/test-kr/cryptoKeys/test-key"
                }
        :type encryption_configuration: dict
        """
        warnings.warn(
            "This method is deprecated, please use ``BigQueryHook.update_table`` method.",
            DeprecationWarning,
        )