要将简单的JSON解析为CSV格式，您可以使用AWS的Athena和Glue服务。以下是解决方案的步骤和代码示例：

1. 创建表和数据库：在Athena中创建一个数据库和表，用于存储解析后的JSON数据。

CREATE DATABASE IF NOT EXISTS mydatabase;
USE mydatabase;

CREATE EXTERNAL TABLE IF NOT EXISTS mytable (
  col1 string,
  col2 int,
  col3 string
)
ROW FORMAT SERDE 'org.openx.data.jsonserde.JsonSerDe'
WITH SERDEPROPERTIES (
  'serialization.format' = '1'
) LOCATION 's3://mybucket/myfolder/'
TBLPROPERTIES ('has_encrypted_data'='false');

2. 创建ETL脚本：使用Glue创建一个ETL脚本，将JSON数据转换成CSV格式，并将其加载到Athena中。

import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.job import Job

args = getResolvedOptions(sys.argv, ['JOB_NAME'])
sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = Job(glueContext)
job.init(args['JOB_NAME'], args)

datasource0 = glueContext.create_dynamic_frame.from_catalog(database = "mydatabase", table_name = "mytable")
applymapping1 = ApplyMapping.apply(frame = datasource0, mappings = [("col1", "string", "col1", "string"), ("col2", "int", "col2", "int"), ("col3", "string", "col3", "string")], transformation_ctx = "applymapping1")
resolvechoice2 = ResolveChoice.apply(frame = applymapping1, choice = "make_cols", transformation_ctx = "resolvechoice2")
dropnullfields3 = DropNullFields.apply(frame = resolvechoice2, transformation_ctx = "dropnullfields3")
datasink4 = glueContext.write_dynamic_frame.from_catalog(frame = dropnullfields3, database = "mydatabase", table_name = "mytable_csv", transformation_ctx = "datasink4")

job.commit()

3. 运行ETL脚本：将ETL脚本上传到AWS Glue，并运行它以将JSON数据转换为CSV格式并加载到Athena中。

4. 查询数据：在Athena中查询转换后的CSV数据。

SELECT * FROM mydatabase.mytable_csv;

以上是使用Athena和Glue将简单的JSON解析为CSV格式的解决方案和代码示例。您可以根据您的具体需求和JSON结构进行适当的调整。