Pyspark script for downloading a single parquet file from Amazon S3 via the s3a protocol. It also reads the credentials from the "~/.aws/credentials", so we don't need to hardcode them. See also https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/index.html .

load_parquet_s3.py

#
# Some constants
#
aws_profile = "your_profile"
aws_region = "your_region"
s3_bucket = "your_bucket"

# 
# Reading environment variables from aws credential file 
#
import os
import configparser

config = configparser.ConfigParser()
config.read(os.path.expanduser("~/.aws/credentials"))

access_id = config.get(aws_profile, "aws_access_key_id") 
access_key = config.get(aws_profile, "aws_secret_access_key") 

# 
# Configuring pyspark
#

# see https://github.com/jupyter/docker-stacks/issues/127#issuecomment-214594895 
# and https://github.com/radanalyticsio/pyspark-s3-notebook/blob/master/s3-source-example.ipynb
os.environ['PYSPARK_SUBMIT_ARGS'] = "--packages=org.apache.hadoop:hadoop-aws:2.7.3 pyspark-shell"

# If this doesn't work you might have to delete your ~/.ivy2 directory to reset your package cache.
# (see https://github.com/databricks/spark-redshift/issues/244#issuecomment-239950148)
import pyspark
sc=pyspark.SparkContext()
# see https://github.com/databricks/spark-redshift/issues/298#issuecomment-271834485
sc.setSystemProperty("com.amazonaws.services.s3.enableV4", "true")

# see https://stackoverflow.com/questions/28844631/how-to-set-hadoop-configuration-values-from-pyspark
hadoop_conf=sc._jsc.hadoopConfiguration()
# see https://stackoverflow.com/questions/43454117/how-do-you-use-s3a-with-spark-2-1-0-on-aws-us-east-2
hadoop_conf.set("fs.s3a.impl", "org.apache.hadoop.fs.s3a.S3AFileSystem")
hadoop_conf.set("com.amazonaws.services.s3.enableV4", "true")
hadoop_conf.set("fs.s3a.access.key", access_id)
hadoop_conf.set("fs.s3a.secret.key", access_key)

# see http://blog.encomiabile.it/2015/10/29/apache-spark-amazon-s3-and-apache-mesos/
hadoop_conf.set("fs.s3a.connection.maximum", "100000")

# see https://docs.aws.amazon.com/general/latest/gr/rande.html#s3_region
hadoop_conf.set("fs.s3a.endpoint", "s3." + aws_region + ".amazonaws.com")

#
# Downloading the parquet file
#
sql=pyspark.sql.SparkSession(sc)
path = s3_bucket + "your_path"
dataS3=sql.read.parquet("s3a://" + path)

Try to use "s3a" and not "s3" .