A company stores millions of 1 KB JSON test-result files in S3 from global testing facilities. A data engineer must convert those files to Apache Parquet and load them into Amazon Redshift. The current pipeline uses AWS Glue for processing, Step Functions for orchestration, and EventBridge for scheduling. As more facilities are added, processing time has increased. Which change will most reduce the processing time?

The correct option is to use the AWS Glue dynamic frame file-grouping option. This directly addresses the "small files problem" common with many small files (1 KB JSON files), which leads to increased processing overhead in distributed systems like AWS Glue. Grouping these small files into larger ones during ingestion significantly improves processing efficiency and reduces job runtime. The other options are less effective: Using AWS Lambda to group files adds an extra, potentially complex, and costly step before Glue processing. Glue's built-in grouping is more efficient. Using the Amazon Redshift COPY command directly on 1 KB JSON files would still suffer from the small files problem and is not suitable for processing; COPY is for loading, not transforming. Using Amazon EMR instead of AWS Glue would be a significant architectural change, potentially requiring re-engineering the entire pipeline, and doesn't inherently solve the small files problem without explicit grouping logic. While EMR can process large datasets, the core issue here is the inefficiency of processing many tiny files.