A company hosts more than 300 global websites and applications and needs a platform to analyze over 30 TB of clickstream data per day. What should a solutions architect use to ingest and process the clickstream data?

The correct solution leverages Kinesis Data Streams for real-time ingestion of high-volume clickstream data, which is ideal for over 30 TB daily. Kinesis Data Firehose then reliably delivers this streaming data to an Amazon S3 data lake, providing durable and scalable storage. Finally, Amazon Redshift is an excellent choice for analyzing large datasets, offering powerful analytics capabilities. Incorrect options: AWS Data Pipeline is primarily for scheduled, batch-oriented data movement and processing, not real-time streaming data ingestion. EMR is suitable for analytics but the ingestion method is wrong. Using an Auto Scaling group of EC2 instances for processing is overly complex and less managed than Kinesis services for streaming data. Caching with CloudFront is for content delivery, not data ingestion or processing. While S3 stores the data, using Lambda for processing 30 TB daily is not cost-effective or scalable enough for continuous, high-volume streaming data.