A data engineering team runs a nightly batch job that processes 8 TB of raw JSON logs stored in Amazon S3. The transformation logic is written in custom Apache Spark and Scala code that relies on several third-party JAR libraries and requires fine-grained tuning of Spark executor memory and shuffle configurations. The job runs once per day for about two hours and cost efficiency is a priority. Which approach best meets these requirements?