The problem states that AWS Glue jobs are running for a long time, which in turn slows down Amazon QuickSight queries. The goal is to improve the performance of the Glue jobs.

1. Partitioning Data (A): Partitioning the data in Amazon S3 by a logical key, such as date (year, month, day), is a fundamental performance optimization technique. When the Glue job runs, it can use partition pruning to read only the specific folders (partitions) of data it needs, rather than scanning the entire dataset in the S3 bucket. This dramatically reduces the amount of data read, which is often the primary bottleneck, thus speeding up the job.

2. Scaling Up Workers (B): AWS Glue jobs run on a serverless Apache Spark environment. Increasing the worker type (e.g., from G.1X to G.2X) provides each worker node with more vCPU, memory, and disk space. This vertical scaling is effective for jobs that are memory-intensive or CPU-bound, allowing them to process data faster.

1. AWS Glue Developer Guide

"Managing partitions for ETL output in AWS Glue": "You can partition your data to speed up queries. AWS Glue can prune the partitions that are not required by a query

which can speed up queries on large datasets." This directly supports option A.

2. AWS Glue Developer Guide

"Job properties": This section details the Worker type parameter. It states

"For memory-intensive jobs

you can specify the G.1X

G.2X

G.4X

G.8X

or Z.2X worker types... Choosing a more powerful worker type can help your job run faster." This directly supports option B.

3. AWS Glue Developer Guide

"Working with DynamicFrames": This documentation describes DynamicFrames as the fundamental data structure used for ETL operations in AWS Glue

not as a performance tuning destination. This refutes option C.