If you have spark session, you can use Spark hidden File System:

# Get FileSystem from SparkSession
fs = spark._jvm.org.apache.hadoop.fs.FileSystem.get(spark._jsc.hadoopConfiguration())
# Get Path class to convert string path to FS path
path = spark._jvm.org.apache.hadoop.fs.Path
 
# List files
fs.listStatus(path("/path/to/data")) # Should work with mounted points
# Rename file
fs.rename(path("OriginalName"), path("NewName"))
# Delete file
fs.delete(path("/path/to/data"))
# Upload file to DBFS root
fs.copyFromLocalFile(path(local_file_path), path(remote_file_path))
# Upload file to DBFS root
fs.copyToLocalFile(path(remote_file_path), path(local_file_path))

 If you have an Azure Storage, you should mount it to you cluster and then you can access it with either `abfss://` or `/mnt/`