A company has large S3 datasets (historical and streaming) that must be ingested into SageMaker Feature Store so the online store reflects recent data immediately and an offline store maintains a complete history. Which solution satisfies these requirements?

The Feature Store Spark connector is the most efficient solution for ingesting large S3 datasets, both historical and streaming, into SageMaker Feature Store. It allows you to process data as Spark DataFrames, which is ideal for big data operations. By enabling both the online and offline stores, you ensure that recent data is immediately available for real-time inference (online store) and a complete historical record is maintained for training and batch inference (offline store). Using the PutRecord API directly for all data is inefficient for large datasets, as it's designed for individual record updates, not bulk ingestion. Ingesting only into the online store would neglect the requirement for a complete historical record in the offline store. Ingesting only into the offline store would fail to provide recent data immediately for the online store.