Apache Spark 3 for Data Engineering and Analytics with Python - Managing Performance Errors

The video tutorial addresses the challenges of using Apache Spark on a single node, highlighting potential errors such as disk block manager issues. It provides solutions for stopping and restarting Spark sessions to resolve these errors. The tutorial also covers managing large files by converting code cells to raw text to prevent errors. Finally, it suggests restarting the entire notebook or system to clean up rogue processes and ensure sufficient disk space.

What are some drawbacks of working with Spark on a single node computer?

How does Spark handle cleanup of temporary space after an error?

What steps should be taken to stop a Spark session when encountering a disk block manager error?

What is the significance of turning cells to raw text in Spark?

What should you do if problems persist after restarting the Spark session?