How can I improve the performance of my small Spark jobs, assuming I am working with data smaller than 10GB?
To improve the performance of small Spark jobs with data smaller than 10GB, you can consider the following options:

1. Run multiple operations in parallel using Delta Live Tables, which does this automatically.
2. Separate your operations into multiple notebooks and run them in parallel on the same cluster by using multi-task jobs.
3. Use Python's ThreadPoolExecutor or another multi-threading approach to run queries in parallel.
4. Use SQL warehouses if all your queries are written in SQL, as they scale well for many queries run in parallel.

These approaches can help reduce the overhead per operation and improve the overall performance of your small Spark jobs.