Within the Spark UI, what would you inspect to diagnose performance issues? Additionally, what are some ways to optimize a notebook, and what types of standards or best practices apply?

In the Spark UI you first look at the Stages tab to spot stages with long durations or many tasks. The Executors tab shows memory usage, spill rates, and task failures. The SQL tab reveals query plans, shuffle sizes, and join types. Inspect the Storage tab for cached RDDs and their persistence levels. If a stage shows many shuffle read/write operations, consider broadcast joins or repartitioning. For notebook optimization, cache intermediate DataFrames only when reused, avoid wide transformations that trigger full shuffles, and use partitioning to reduce data scanned. Prefer DataFrame/Dataset APIs over RDDs for built‑in optimizations. Use vectorized UDFs or native functions instead of Python UDFs to keep execution in JVM. Apply join hints (broadcast, shuffle_hash) when appropriate. In terms of standards, follow coding conventions (clear naming, modular functions), document notebook cells, version‑control notebooks, and enforce unit tests for critical transformations. Maintain a consistent data schema and use schema evolution best practices. Finally, monitor resource usage and set appropriate executor memory and cores to match workload.