describe_dataset ¶

def describe_dataset(
    self,
    name: "capo_databrew.types.dataset_name.DatasetName",
    *,
    config_overrides: Optional[DataBrewClientConfig] = None,
) -> "capo_databrew.types.describe_dataset_response.DescribeDatasetResponse":
    """<p>Returns the definition of a specific DataBrew dataset.</p>

    Args:
        name: <p>The name of the dataset to be described.</p>

    Raises:
        capo_databrew.errors.resource_not_found_exception.ResourceNotFoundException: <p>One or more resources can't be found.</p>
        capo_databrew.errors.validation_exception.ValidationException: <p>The input parameters for this request failed validation.</p>
        capo_databrew.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_databrew.types.describe_dataset_request.DescribeDatasetRequest]",
    ) -> OperationResponse[
        "capo_databrew.types.describe_dataset_response.DescribeDatasetResponse"
    ]:
        import capo_databrew._operations.aws_glue_data_brew.describe_dataset

        output, http_response = (
            capo_databrew._operations.aws_glue_data_brew.describe_dataset.describe_dataset(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("DescribeDataset")
    )
    input_: capo_databrew.types.describe_dataset_request.DescribeDatasetRequest = {
        "name": name
    }

    response = execute_pipeline(
        OperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    response.response.close()
    return response.output