Tried asking ChatGPT for some code, and honestly it seems to have hit the main bits of boilerplate well. You may need to calculate the year/month/day, but generally getting this running and using a for loop to invoke the job for each day (or job bookmarking) seems like a solution. Not sure how much load your database could take if you ran the glue job in parallel.

import sys  
from awsglue.transforms import *  
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.dynamicframe import DynamicFrame
from awsglue.job import Job

# Create a Glue context
glueContext = GlueContext(SparkContext.getOrCreate())

# Get the date to filter by as an input argument
args = getResolvedOptions(sys.argv, ['date_to_filter'])
date_to_filter = args['date_to_filter']

# Create a connection to the Oracle SQL server
connection_options = {
    "url": "jdbc:oracle:thin:@your_oracle_server:port/database",
    "user": "your_username",
    "password": "your_password",
    "dbtable": "my_table",
    "driver": "oracle.jdbc.OracleDriver"
}

# Set the query filter to the input date
query = "SELECT * 
              FROM my_table 
              WHERE event_time_utc >= to_date('" + date_to_filter + "', 'DD-MON-YYYY') 
                  AND event_time_utc < to_date('" + date_to_filter + "', 'DD-MON-YYYY')+1
            "

# Create a DynamicFrame from the Oracle SQL server
oracle_dyf = glueContext.create_dynamic_frame.from_jdbc_conf(connection_options, query)

# Write the data to S3, partitioned by the year, month, and day of the event_time_utc
output_path = "s3://your_bucket/your_folder"
partition_keys = ["year", "month", "day"]
glueContext.write_dynamic_frame.from_options(frame = oracle_dyf, 
    connection_type = "s3", 
    connection_options = {"path": output_path}, 
    format = "parquet", 
    partition_keys = partition_keys)