A company with a video streaming website wants to analyze user behavior to make recommendations to users in real time. Clickstream data is being sent to Amazon Kinesis Data Streams and reference data is stored in Amazon S3. The company wants a solution that can use standard SOL queries. The solution must also provide a way to look up pre-calculated reference data while making recommendations.
Which solution meets these requirements?
A. Use an AWS Glue Python shell job to process incoming data from Kinesis Data Streams. Use the Boto3 library to write data to Amazon Redshift.
B. Use AWS Glue streaming and Scala to process incoming data from Kinesis Data Streams. Use the AWS Glue connector to write data to Amazon Redshift.
C. Use Amazon Kinesis Data Analytics to create an in-application table based upon the reference data. Process incoming data from Kinesis Data Streams. Use a data stream to write results to Amazon Redshift.
D. Use Amazon Kinesis Data Analytics to create an in-application table based upon the reference data. Process incoming data from Kinesis Data Streams. Use an Amazon Kinesis Data Firehose delivery stream to write results to Amazon Redshift.
Which solution meets these requirements?
A. Use an AWS Glue Python shell job to process incoming data from Kinesis Data Streams. Use the Boto3 library to write data to Amazon Redshift.
B. Use AWS Glue streaming and Scala to process incoming data from Kinesis Data Streams. Use the AWS Glue connector to write data to Amazon Redshift.
C. Use Amazon Kinesis Data Analytics to create an in-application table based upon the reference data. Process incoming data from Kinesis Data Streams. Use a data stream to write results to Amazon Redshift.
D. Use Amazon Kinesis Data Analytics to create an in-application table based upon the reference data. Process incoming data from Kinesis Data Streams. Use an Amazon Kinesis Data Firehose delivery stream to write results to Amazon Redshift.