使用Google Cloud Storage将文本文件上传至云端后，再通过Big Query进行导入。

代码示例：

1. 上传文本文件至Google Cloud Storage

from google.cloud import storage

def upload_to_gcs(bucket_name, local_file_path, destination_blob_name):
    """将本地文件上传至Google Cloud Storage"""
    storage_client = storage.Client()
    bucket = storage_client.bucket(bucket_name)
    blob = bucket.blob(destination_blob_name)
    blob.upload_from_filename(local_file_path)
    
# 示例
bucket_name = 'my_bucket'
local_file_path = '/path/to/local/file.csv'
destination_blob_name = 'uploaded_file.csv'
upload_to_gcs(bucket_name, local_file_path, destination_blob_name)

2. 从Google Cloud Storage导入数据至Big Query表格

from google.cloud import bigquery

def import_to_bigquery(dataset_id, table_id, source_url):
    """将Google Cloud Storage中的文本文件导入至Big Query表格"""
    client = bigquery.Client()
    dataset_ref = client.dataset(dataset_id)
    job_config = bigquery.LoadJobConfig()
    job_config.source_format = bigquery.SourceFormat.CSV
    job_config.skip_leading_rows = 1  # 跳过CSV文件中的第一行表头
    uri = source_url.split('/')[3:]  # 格式为 gs://bucket-name/filepath
    uri = '/'.join(uri)
    uri = f"gs://{uri}"
    load_job = client.load_table_from_uri(
        uri, dataset_ref.table(table_id), job_config=job_config
    )
    
    # 将上传任务状态打印输出，使用result函数等待任务完成
    print(f"Starting job {load_job.job_id}")
    load_job.result()
    print(f"Job {load_job.job_id} completed.")
    
# 示例
dataset_id = 'my_dataset'
table_id = 'my_table'
source_url = 'gs://my_bucket/uploaded_file.csv'
import_to_bigquery(dataset_id, table_id, source_url)