A transportation company uses IoT sensors attached to trucks to collect vehicle data for its global delivery fleet. The company currently sends the sensor data in small .csv files to Amazon S3. The…

While converting to Parquet is good, creating a single large Parquet file will prevent parallel loading into a multi-slice Amazon Redshift cluster, significantly slowing down the ingestion process as only one slice can process the single file. It also limits query parallelism for services like Athena.

Apache Avro is a row-oriented format, which is generally less efficient than columnar formats like Parquet or ORC for analytical queries that often select a subset of columns. Using Amazon EMR for this task is also less managed than AWS Glue, and converting 'each .csv file' might still lead to many small Avro files, negating consolidation benefits.

Similar to option A, converting to a single large Apache ORC file will bottleneck data loading into Amazon Redshift by preventing parallel ingestion across the cluster's slices. While ORC is a good columnar format, the file sizing is suboptimal for Redshift's parallel loading architecture.

Converting small CSV files into Apache Parquet using AWS Glue improves query performance and reduces costs for both Amazon Athena and Redshift Spectrum due to its columnar storage and compression. Crucially, creating 20 Parquet files (matching the 20 slices in the 10-node Redshift cluster) enables highly parallel and efficient data loading into Redshift using the COPY command, directly addressing all requirements.