在AWS中实现ETL转换可以使用AWS Glue。在Glue中，可以使用Python编写ETL脚本，并将其提供给Glue数据泵，以将数据从源系统源到目标系统中。 以下是使用AWS Glue实现ETL转换的代码示例：

首先需要定义源数据和目标数据的架构。您可以使用AWS Glue数据目录来定义数据架构。

from awsglue.context import GlueContext
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from pyspark.sql.functions import *
from pyspark.sql.types import *

sc = SparkContext()
glueContext = GlueContext(sc)
sc.setLogLevel('ERROR')

args = getResolvedOptions(sys.argv, ['JOB_NAME'])
job_name = args['JOB_NAME']

source_database = "source-database" 
source_table = "source-table"
source_path = "s3://bucket/source"
source_format = "avro"
source_partition_keys = ['year', 'month', 'day']

target_database = 'target-database'
target_table = 'target-table'
target_path = 's3://bucket/target'
target_format = 'parquet'

定义好架构后，接下来需要完成ETL转换的三个过程：提取，转换和加载。

1. 提取数据

source = glueContext.create_dynamic_frame.from_catalog(
           database = source_database,
           table_name = source_table,
           transformation_ctx = "source"
)

apply_mapping = ApplyMapping.apply(frame = source,
           mappings = [
               ("col1", "string", "col_a", "string"),
               ("col2", "string", "col_b", "string"),
               ("col3", "int", "col_c", "int"),
           ],
           transformation_ctx = "apply_mapping"
)

上面的代码将从源数据中提取所需的列，并将其映射到目标表中的列。

2. 转换数据

apply_transforms = ResolveChoice.apply(frame = apply_mapping, 
                   choice = "make_cols",
                   transformation_ctx = "apply_transforms"
)

drop_nulls = DropNullFields.apply(frame = apply_transforms,
                   transformation_ctx = "drop_nulls"
)

target = glueContext.write_dynamic_frame.from_options(
           frame = drop_nulls,
           connection_type = "s3",
           connection_options = {"path": target_path},
           format = target_format,
           transformation_ctx = "target"
)

上面的代码将ETL转换应用于源数据，