A data engineer must ingest real-time streaming data and run time-based analytics with windows up to 30 minutes. The solution must be highly fault tolerant and require minimal operational overhead. Which option satisfies these requirements?

The correct answer is to use Amazon Managed Service for Apache Flink. This service is specifically designed for real-time stream processing and offers robust capabilities for time-based analytics, including various windowing functions (like tumbling, sliding, and session windows) and aggregations. It is highly fault-tolerant, automatically scales, and has minimal operational overhead, aligning with the requirements. Using AWS Lambda for time-based aggregations over a 30-minute window is generally not ideal for continuous real-time stream processing. Lambda functions are stateless and event-driven, making it complex and inefficient to maintain state across invocations for windowed aggregations. While Lambda can process Kinesis Data Streams, managing 30-minute windows and fault tolerance for continuous stateful processing would require significant custom development and operational overhead. The options mentioning Lambda are therefore less suitable.