A trucking company collects about 100 GB of new images per day from trucks worldwide. They want to explore ML use cases and ensure that only specific IAM users can access the data. Which storage option gives the most flexibility for processing and allows access control via IAM?

Storing images in an Amazon S3-backed data lake is the most flexible and scalable solution. S3 can handle the 100 GB/day ingestion rate, provides virtually unlimited storage, and integrates seamlessly with various AWS ML services for processing. IAM policies, including bucket policies, offer granular control over who can access the data. DynamoDB is a NoSQL database optimized for key-value and document data, not large binary objects like images, making it unsuitable. HDFS on EMR is a good option for big data processing, but S3 offers more durability, availability, and cost-effectiveness for raw data storage, and EMR clusters are typically transient. Amazon EFS is a shared file system for EC2 instances, but it's not designed for petabyte-scale, high-ingestion-rate object storage like S3, nor does it offer the same level of native integration with ML services.