How to speed up my Glue ETL process?

Forgive me if I am totally wrong here, but I think my job is already doing this. The Visual tool generates an automatic script and I'm seeing the following nodes with the pushdown predicate, such as:

event_ad_view_node1693352511778 = glueContext.create_dynamic_frame.from_catalog(
    database="database",
    push_down_predicate="to_date(dt) >= date_sub(current_date, 2)",
    table_name="event_ad_view",
    transformation_ctx="event_ad_view_node1693352511778",

Does this not imply that it is in fact filtering the dataset using the pushdown predicate as it is creating the dynamic frame?

It's important to note that not all of the tables I am using are partitioned so I am unable to apply a pushdown predicate to all of them. I have yet to find another way to filter on these tables.

Later in the script it uses sparkSqlQuery to pull the query:

SqlQuery0 = """ my query

UserDailyQuery_node1693352569830 = sparkSqlQuery(
    glueContext,
    query=SqlQuery0,
    mapping={
......
}

SparkSQLQuery is defined as the following:

def sparkSqlQuery(glueContext, query, mapping, transformation_ctx) -> DynamicFrame:
    for alias, frame in mapping.items():
        frame.toDF().createOrReplaceTempView(alias)
    result = spark.sql(query)
    return DynamicFrame.fromDF(result, glueContext, transformation_ctx)

Is this the method you were referring to or is there another way?

In this case, you are going to have more control if you use a script job, also the plan on SparkUI will be simpler to understand so you can see where it time spent