A company receives test results from testing facilities that are located around the world. The company stores the test results in millions of 1 KB JSON files in an Amazon S3 bucket. A data engineer…

While grouping small files into larger ones is a good strategy, using AWS Lambda to perform this as a separate step introduces additional operational complexity, latency, and cost compared to Glue's native dynamic frame grouping capability.

Processing millions of small files (1 KB JSON) is inefficient due to overhead associated with opening, closing, and processing each file; the AWS Glue dynamic frame file-grouping option consolidates these small files into larger, more optimal units for processing, which significantly reduces the data processing time within Glue ETL jobs.

The Amazon Redshift COPY command is designed to load data from S3 into Redshift, but it does not perform the necessary transformation from JSON to Apache Parquet format and is not optimized for efficiently handling millions of 1 KB files directly for processing.

Although Amazon EMR can handle large-scale data processing and grouping, switching from AWS Glue to Amazon EMR introduces significant operational overhead for cluster management and re-architecting the existing pipeline, and Glue's dynamic frames offer a more integrated solution for this specific optimization within the existing Glue ecosystem.