How would you use Spark UI and driver logs to debug a Spark job that fails due to data skew or out‑of‑memory errors?

First, open the Spark UI and navigate to the Stages tab. Look for stages with a large number of failed or long‑running tasks; a single task that takes much longer than the others is a classic sign of data skew. Inspect the task metrics for shuffle read/write sizes and see if one partition is much larger. Next, check the driver logs for stack traces such as OutOfMemoryError or SparkException: Task failed. If the error mentions a specific stage or task, correlate that with the UI. To confirm skew, you can enable the spark.sql.shuffle.partitions setting to a higher value and see if the skew persists. If you confirm skew, you can mitigate it by adding a salting column, using a broadcast join, or repartitioning on a key that distributes data more evenly. For OOM, you can increase executor memory, enable off‑heap storage, or use Tungsten‑enabled operations. The combination of UI metrics and driver logs gives you the root cause and a path to remediation.