You work for a software company that has developed a popular mobile gaming app that has a large, active user base. You want to run a predictive model on real-time data generated by the app users to see how to structure an upcoming marketing campaign. The data you need for the model is the user`s age, location, and level of activity in the game as measured by playing time. You need to filter the data for users who have not yet signed up for your company’s premium service. You’ll also need to deliver your data in json format, convert the playing time into a string format, and finally put the data onto an S3 bucket.
Which of the following is the simplest, most cost-effective, performant, and scalable way to architect this data pipeline?

Answer options:

A.Create a Kinesis Data Streams application running on an EC2 instance that gathers the mobile user data from its log files; use Kinesis Analytics to transform the log data into the subset you need; connect the Kinesis Data Stream to a Kinesis Firehose which puts the data onto your S3 bucket.
B.Create a Kinesis Data Streams application running on EC2 instances in an Auto Scaling Group that gathers the mobile user data from its log files; use Kinesis Analytics to transform the log data into the subset you need; connect Kinesis Data Analytics to a Kinesis Firehose which uses a lambda function to convert the playing time; Kinesis Firehose then puts the data onto your S3 bucket.
C.Create a Kinesis Firehose which gathers the data and puts it onto your S3 bucket.
D.Create a Kinesis Data Streams application running on EC2 instances in an Auto Scaling Group that gathers the mobile user data from its log files and puts it onto your S3 bucket.

Answer: B
Option A is incorrect. This option has a bottleneck at the single EC2 instance used to gather the log data from the application log files. This solution would not be the most scalable.
Option B is correct. This option scales well at the Kinesis Data Streams application level because of the Auto Scaling Group. It also uses Kinesis Data Analytics to transform the data into the subset you need. It uses the Kinesis Firehose lambda option to convert the playing time to the proper format.
Option C is incorrect. This option does not transform the log data gathered by the Kinesis Firehose before writing the data to the S3 bucket.
Option D is incorrect. This option does not transform the log data gathered by the Kinesis Data Streams application before writing the data to the S3 bucket.
Reference:
Please see the AWS developer guide documentation titled What is Kinesis Data Streams, the AWS Auto Scaling documentation, the Amazon Kinesis Data Firehose documentation, and the Amazon Kinesis Data Analytics documentation.
Here is a diagram of the solution: