A company has one million mobile app users and needs near-real-time data analysis. The data must be encrypted in near-real time and stored centrally in Apache Parquet format for further processing. Which solution provides this with the LEAST operational overhead?

The correct solution uses Amazon Kinesis Data Firehose and Kinesis Data Analytics. Kinesis Data Firehose is a fully managed service that automatically scales to handle large volumes of streaming data, buffers it, and delivers it to destinations like Amazon S3 in formats like Apache Parquet, with built-in encryption, minimizing operational overhead. Kinesis Data Analytics (now Amazon Managed Service for Apache Flink) is a fully managed service for near-real-time analysis of streaming data, requiring minimal setup and management. Incorrect options: Using a Kinesis data stream requires manual scaling and management, increasing operational overhead compared to Firehose. Sending data to Kinesis Data Analytics via Lambda adds unnecessary complexity and overhead for data ingestion. Using a Kinesis data stream and an Amazon EMR cluster both introduce significant operational overhead. EMR clusters require provisioning, managing, and scaling, which is not ideal for "least operational overhead." While Kinesis Data Firehose is a good choice for ingestion, using an Amazon EMR cluster for analysis contradicts the "least operational overhead" requirement due to its management overhead.