可以使用Apache Beam和Google Cloud Dataflow来处理大量文件，并为文件名添加元数据，例如创建日期和文件大小等。以下是一个示例管道，用于处理Google Cloud Storage中的1M+个.csv文件，并为每个文件添加文件大小并将其写入BigQuery。

import apache_beam as beam

class AddMetadata(beam.DoFn):
    """
    Add metadata to the filename
    """
    def process(self, element):
        from google.cloud import storage
        from datetime import datetime
        import os
        
        bucket_name = 'my-bucket' 
        storage_client = storage.Client()
        bucket = storage_client.get_bucket(bucket_name)
        blob = bucket.get_blob(element)

        # Add file size and creation time metadata to the filename
        filename_with_metadata = f"{element}|{blob.size}|{datetime.fromtimestamp(os.path.getctime(element)).strftime('%Y-%m-%d %H:%M:%S')}"

        return filename_with_metadata

def run_pipeline():

    # Set up the pipeline options
    options = beam.options.pipeline_options.PipelineOptions(
        runner='DataflowRunner',
        region='us-central1',
        project='my-project',
        job_name='add-metadata',
        temp_location='gs://my-bucket/temp',
        setup_file='./setup.py'
    )

    # Create the pipeline
    with beam.Pipeline(options=options) as p:

        # Read filenames from Google Cloud Storage
        files = p | 'Read filenames' >> beam.io.ReadFromText('gs://my-bucket/files.txt')

        # Add filename metadata
        files_with_metadata = files | 'Add metadata' >> beam.ParDo(AddMetadata())

        # Write files with metadata to BigQuery
        files_with_metadata | 'Write to BigQuery' >> beam.io.WriteToBigQuery(
            'my_dataset.my_table',
            schema='filename:STRING,size:INTEGER,created:DATETIME',
            write_disposition=beam.io.BigQueryDisposition.WRITE_TRUNCATE,
            create_disposition=beam.io.BigQueryDisposition.CREATE_IF_NEEDED
        )

if __name__ == '__main__':
    run_pipeline()

该示例管道使用AddMetadata DoFn来获取文件大小和创建日期，并将其添加到文件名中。然后，将文件名和元数据写入BigQuery以进行进一步分析。您可以将此管道更改为处理其他类型的文件，例如图像或日志文件。