A large media company is looking for a cost-effective storage and analysis solution for its daily media recordings formatted with embedded metadata. Daily data sizes range between 10-12 TB with stream analysis required on timestamps, video resolutions, file sizes, closed captioning, audio languages, and more. Based on the analysis, processing the datasets is estimated to take between 30-180 minutes depending on the underlying framework selection. The analysis will be done by using business intelligence (BI) tools that can be connected to data sources with AWS or Java Database Connectivity (JDBC) connectors.
Which solution meets these requirements?

A. Store the video files in Amazon DynamoDB and use AWS Lambda to extract the metadata from the files and load it to DynamoDB. Use DynamoDB to provide the data to be analyzed by the BI tools.
B. Store the video files in Amazon S3 and use AWS Lambda to extract the metadata from the files and load it to Amazon S3. Use Amazon Athena to provide the data to be analyzed by the BI tools.
C. Store the video files in Amazon DynamoDB and use Amazon EMR to extract the metadata from the files and load it to Apache Hive. Use Apache Hive to provide the data to be analyzed by the BI tools.
D. Store the video files in Amazon S3 and use AWS Glue to extract the metadata from the files and load it to Amazon Redshift. Use Amazon Redshift to provide the data to be analyzed by the BI tools.