1. 导入所需Python库：

import boto3
import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from pyspark.sql.functions import *
from awsglue.context import GlueContext
from pyspark.sql import SparkSession
from awsglue.dynamicframe import DynamicFrame

2. 配置AWS Glue作业参数：

args = getResolvedOptions(sys.argv, ['JOB_NAME'])
glueContext = GlueContext(SparkContext.getOrCreate())
job = glueContext.create_job(args['JOB_NAME'], args)

3. 定义源文件路径和目标文件路径：

source_path = "s3://source-bucket/path/to/parquet/files/"
destination_path = "s3://destination-bucket/path/to/parquet/files/"

4. 读取数据：

in_dyf = glueContext.create_dynamic_frame_from_options(
            "parquet", {"paths": [source_path]}, "my_source")

5. 构建转换器：

applymapping1 = ApplyMapping.apply(frame=in_dyf, mappings=[
    # Mapping column names in source to column names in destination
    ("field_name_in_source", "string", "field_name_in_destination", "string"),
    ("field_name_in_source2", "string", "field_name_in_destination2", "string")
], transformation_ctx="applymapping1")

6. 转换数据类型：

cast3 = applymapping1.toDF().withColumn(
    "numeric_column_name", col("numeric_column_name").cast("decimal(10,2)"))

7. 写入数据：

out_dyf = DynamicFrame.fromDF(cast3, glueContext, "out_dyf")
glueContext.write_dynamic_frame.from_options(frame=out_dyf, connection_type="s3",
                                             connection_options={"path":destination_path},
                                             format="parquet",
                                             transformation_ctx="out")