CreateConfiguredTableInput ¶

class CreateConfiguredTableInput(TypedDict, closed=True):
    name: "capo_cleanrooms.types.display_name.DisplayName"
    """<p>The name of the configured table.</p>"""
    description: NotRequired["capo_cleanrooms.types.table_description.TableDescription"]
    """<p>A description for the configured table.</p>"""
    table_reference: "capo_cleanrooms.types.table_reference.TableReference"
    """<p>A reference to the table being configured.</p>"""
    allowed_columns: "capo_cleanrooms.types.allowed_column_list.AllowedColumnList"
    """<p>The columns of the underlying table that can be used by collaborations or analysis rules.</p>"""
    analysis_method: "capo_cleanrooms.types.analysis_method.AnalysisMethod"
    """<p>The analysis method allowed for the configured tables.</p> <p> <code>DIRECT_QUERY</code> allows SQL queries to be run directly on this table.</p> <p> <code>DIRECT_JOB</code> allows PySpark jobs to be run directly on this table.</p> <p> <code>MULTIPLE</code> allows both SQL queries and PySpark jobs to be run directly on this table.</p>"""
    selected_analysis_methods: NotRequired[
        "capo_cleanrooms.types.selected_analysis_methods.SelectedAnalysisMethods"
    ]
    """<p> The analysis methods to enable for the configured table. When configured, you must specify at least two analysis methods.</p>"""
    tags: NotRequired["capo_cleanrooms.types.tag_map.TagMap"]
    """<p>An optional label that you can assign to a resource when you create it. Each tag consists of a key and an optional value, both of which you define. When you use tagging, you can also use tag-based access control in IAM policies to control access to this resource.</p>"""