What should a solutions architect do to transmit and process the clickstream data?
Design an AWS Data Pipeline to archive the data to an Amazon S3 bucket and run an Amazon EMR cluster with the data to generate analytics.
Create an Auto Scaling group of Amazon EC2 instances to process the data and send it to an Amazon S3 data lake for Amazon Redshift to use for analysis.
Cache the data to Amazon CloudFront. Store the data in an Amazon S3 bucket. When an object is added to the S3 bucket. run an AWS Lambda function to process the data for analysis.
Collect the data from Amazon Kinesis Data Streams. Use Amazon Kinesis Data Firehose to transmit the data to an Amazon S3 data lake. Load the data in Amazon Redshift for analysis.
Explanations:
AWS Data Pipeline is not optimized for real-time processing of clickstream data, making it less suitable for high-volume, daily analytics tasks.
While Auto Scaling EC2 can process data, it may not efficiently handle the high volume of clickstream data compared to more specialized services. Additionally, this approach can lead to higher operational overhead.
Caching with CloudFront and using Lambda for processing is not ideal for handling large volumes of data continuously and would not efficiently process 30 TB of clickstream data daily.
Using Amazon Kinesis Data Streams for real-time data ingestion, combined with Kinesis Data Firehose to load data into S3 and then into Redshift, is a scalable and efficient solution for processing large volumes of clickstream data.