Suppose your AWS Glue job takes a very long time (e.g., 20 minutes or more). How would you investigate and optimize its performance?

First, enable detailed job metrics in the Glue console and review CloudWatch logs for errors, stage durations, and shuffle sizes. Check the job’s Spark UI (via the Glue console or the underlying EMR cluster) to see which stages consume the most time. Common culprits are large shuffle operations, insufficient memory, or unpartitioned data. If the source data is in CSV or JSON, convert it to a columnar format like Parquet or ORC and enable compression (snappy or gzip) to reduce I/O. Partition the source table on a high‑cardinality column (e.g., date or region) so the job reads only relevant partitions. Enable dynamic allocation and tune the number of executors (e.g., set spark.executor.instances and spark.executor.memory). Use --enable-continuous-cloudwatch-log to stream logs for real‑time monitoring. If the job is reading from S3, enable S3 Select or use the new Glue “Push‑down” feature to filter rows at the storage layer. Consider upgrading to Glue version 2, which uses a lighter runtime and can reduce startup time. Finally, profile the job with the Glue profiler or Spark UI to identify bottlenecks, then iterate on the above changes. After each change, run a small test job to confirm the performance improvement before scaling to full data volume.