AWS实时ETL管道的最合适的架构可以使用AWS Lambda、Amazon Kinesis Data Streams和Amazon DynamoDB来实现。以下是一个包含代码示例的解决方法：

1. 创建一个Kinesis数据流：

   import boto3
   
   kinesis_client = boto3.client('kinesis')
   
   response = kinesis_client.create_stream(
       StreamName='my-stream',
       ShardCount=1
   )
   

2. 创建一个DynamoDB表用于存储事务数据：

   dynamodb_client = boto3.client('dynamodb')
   
   response = dynamodb_client.create_table(
       TableName='my-table',
       AttributeDefinitions=[
           {
               'AttributeName': 'transaction_id',
               'AttributeType': 'N'
           },
       ],
       KeySchema=[
           {
               'AttributeName': 'transaction_id',
               'KeyType': 'HASH'
           },
       ],
       ProvisionedThroughput={
           'ReadCapacityUnits': 5,
           'WriteCapacityUnits': 5
       }
   )
   

3. 创建一个Lambda函数用于处理Kinesis数据流并将数据写入DynamoDB表：

   import boto3
   
   dynamodb = boto3.resource('dynamodb')
   table = dynamodb.Table('my-table')
   
   def lambda_handler(event, context):
       for record in event['Records']:
           transaction_id = record['Data']
           table.put_item(Item={'transaction_id': int(transaction_id)})
   

4. 创建一个Kinesis数据流消费者Lambda函数来触发实时ETL过程：

   import boto3
   
   kinesis_client = boto3.client('kinesis')
   lambda_client = boto3.client('lambda')
   
   def lambda_handler(event, context):
       response = kinesis_client.describe_stream(
           StreamName='my-stream'
       )
       shard_iterator = kinesis_client.get_shard_iterator(
           StreamName='my-stream',
           ShardId=response['StreamDescription']['Shards'][0]['ShardId'],
           ShardIteratorType='TRIM_HORIZON'
       )['ShardIterator']
   
       while True:
           response = kinesis_client.get_records(
               ShardIterator=shard_iterator,
               Limit=100
           )
   
           records = response['Records']
           if len(records) == 0:
               break
   
           payload = {
               'Records': records
           }
   
           lambda_client.invoke(
               FunctionName='my-etl-lambda-function',
               InvocationType='Event',
               Payload=json.dumps(payload)
           )
   
           shard_iterator = response['NextShardIterator']
   

通过上述架构，数据将从Kinesis数据流传递到Lambda函数中，然后将数据写入DynamoDB表中。您可以根据自己的需求进行必要的修改和调整。