A company is building a new data platform that will ingest real-time streaming data from multiple sources. The data must be transformed before being written to Amazon S3, and the company needs to run SQL queries against the transformed data. Which solutions meet these requirements? (Choose two.)

The first correct option leverages the Kinesis suite: Kinesis Data Streams for real-time ingestion, Kinesis Data Analytics for stream processing and transformation, Kinesis Data Firehose for delivering the transformed data to S3, and Athena for querying S3 data with SQL. This is a complete, serverless, and scalable streaming data pipeline. The second correct option uses MSK for ingestion, providing a fully managed Apache Kafka experience. AWS Glue is a powerful, serverless ETL service suitable for transforming data before writing to S3, and Athena again serves as the SQL query engine for S3. The incorrect options fail for several reasons. AWS DMS is for database migration, not general real-time streaming ingestion. Using Amazon RDS query editor to query data directly from S3 is not a standard or efficient pattern; Athena is designed for this. While Kinesis Data Analytics can transform data, it typically integrates with Firehose or other destinations, not directly writing to S3 as a primary transformation and storage mechanism in the context of the given options.