I [recently had a issue with with one of my spark jobs](https://stackoverflow.com/questions/69376044/pyspark-job-failing-while-reading-parquet-file-with-billions-of-records-in-aws-e), where I was reading a hive table having several billion records, that resulted in job failure due to high disk utilization, But after adding AWS EBS volume, the job ran without any issues. Although it resolved the issue, I have few doubts, I tried doing some research but couldn't find any clear answers. So my question is?

when a spark SQL reads a hive table, where the data is stored for processing initially and what is the entire life cycle of data in terms of its storage , if I didn't explicitly specify anything? And How adding EBS volumes solves the issue?