An online advertising company wants to perform sentiment analysis of social media data to measure the success of online advertisements. The company wants to implement an end-to-end streaming solution to continuously ingest data from various social networks, clean and transform the streaming data in near-real time, and make the data available for analytics and visualization with Amazon QuickSight. The company wants a solution that is easy to implement and manage so it can design better analytics solutions instead of provisioning and maintaining infrastructure.
Which solution meets these requirements with the LEAST amount of operational effort?

A. Use Amazon Kinesis Data Firehose to ingest the data. Author an AWS Glue streaming ETL job to transform the ingested data. Load the transformed data into an Amazon Redshift table.
B. Use Apache Kafka running on Amazon EC2 instances to ingest the data. Create an Amazon EMR Spark job to transform the ingested data. Use the COPY command to load the transformed data into an Amazon Redshift table.
C. Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) to ingest the data. Create an Amazon EMR Spark job to transform the ingested data. Use the COPY command to load the transformed data into an Amazon Redshift table.
D. Use Amazon Kinesis Data Streams to ingest the data. Author an AWS Glue streaming ETL job to transform the ingested data. Load the transformed data into an Amazon Redshift table.