How can a data analyst improve the execution time of an AWS Glue job that is running too long?

Enabling job metrics in AWS Glue and increasing the maximum capacity job parameter based on profiling is a sound strategy for improving the execution time of a Glue job. Monitoring job metrics allows data analysts to gain insights into performance bottlenecks and understand how resources are being utilized during the job run. By analyzing these metrics, analysts can make informed decisions about resource allocation.

Increasing the maximum capacity job parameter directly impacts the amount of processing power allocated to the job, which can lead to faster execution times as more resources are available to handle data processing tasks concurrently. This adjustment, based on the findings from the job metrics, ensures the job is appropriately sized for the workload, potentially reducing the overall processing time.

Other options do not directly address performance enhancement based on execution profiling. For instance, increasing the default timeouts of the job does not improve execution speed; it merely allows more time for the job to complete without failure. Upgrading the job definition to use a G3 worker type may provide better performance, but this requires a predefined environment and may not be sufficient without proper metrics analysis. Scheduling the job to run during off-peak hours could alleviate contention for resources but doesn’t inherently speed up processing times; rather, it depends on the availability of resources at that time.