Fastest way to save large SQL Server table to S3 - sql-server

Question

I am trying to save a 200GB table from a Microsoft SQL Server 2012 instance.
At the moment trying to use pyspark over a EMR cluster.
if __name__ == '__main__':
# sc = SparkContext()
spark = SparkSession \
.builder \
.appName("Spark camt52") \
.config("spark.sql.adaptive.enabled", "true") \
.config("spark.dynamicAllocation.enabled", "true") \
.config("spark.sql.streaming.schemaInference", "true") \
.getOrCreate()
spark.sparkContext.setLogLevel("WARN")
jdbcDf = (spark.read
.format("jdbc")
.option("url",
"jdbc:sqlserver://servr:1433;databaseName=XXX")
.option("driver", "com.microsoft.sqlserver.jdbc.SQLServerDriver")
.option("dbtable", "XXX.dbo.MyTable")
.option("user", "MyUser")
.option("password", "MyPass").load())
print(jdbcDf.count())
jdbcDf.write.mode("overwrite").parquet("s3://dump_bucket/tables/bigtable/")
Cluster Config:
'InstanceFleets': [
{
'Name': 'MASTER',
'InstanceFleetType': 'MASTER',
'TargetSpotCapacity': 1,
'InstanceTypeConfigs': [
{
'InstanceType': 'm5.xlarge',
},
]
},
{
'Name': 'CORE',
'InstanceFleetType': 'CORE',
'TargetSpotCapacity': 8,
'InstanceTypeConfigs': [
{
'InstanceType': 'r5.2xlarge',
},
],
},
],
Its been running for over 2h. I am still getting the hang of EMR and pyspark so maybe messed something up. Should I use a larger cluster maybe?