A media company will migrate its user activity data store to AWS. The data store will grow to petabytes. The company needs a highly available data ingestion solution that supports on-demand SQL analytics of existing and new data and requires the LEAST operational overhead. Which solution meets these requirements?

The correct solution uses Amazon Kinesis Data Firehose to ingest data and Amazon Redshift for analytics. Kinesis Data Firehose is a fully managed service that automatically scales to handle large data volumes, minimizing operational overhead. It can directly deliver data to Amazon Redshift, which is a petabyte-scale data warehouse optimized for SQL analytics. This combination provides a highly available, scalable, and low-overhead solution for ingesting and analyzing petabytes of data. Sending data to a Kinesis data stream and then to S3 would require additional services (like Amazon Athena or Redshift Spectrum) for SQL analytics, increasing operational overhead. Placing data directly in S3 and using Lambda for processing would also require additional steps for SQL analytics and might not be as efficient for continuous, high-volume ingestion as Firehose. Creating an ingestion service on EC2 and using Amazon RDS would involve significant operational overhead for managing EC2 instances and RDS is not designed for petabyte-scale analytical workloads.