Exam AWS Certified Solutions Architect - Associate SAA-C02 topic 1 question 405 discussion

Ans. B - address all points - realtime, clickstream data, additionally serverless and data persisted, C doesn't address Data analysis part also not serverless or how data got persisted. A and D - not apprporiate.

Two whole different use cases:- Athena queries historical data from S3. Kinesis Data Analytics processed real-time streaming data. Hence going with B.

B is the answer

For the given use case, post the real-time analysis, the output feed from Kinesis Data Analytics is output into Kinesis Data Firehose which dumps the data into Amazon S3 without any data loss. - option B Athena cannot be used to analyze data in real time. Therefore option A is inappropriate.

A is correct

Athena uses sql

B is better With Amazon Kinesis Data Analytics for SQL Applications, you can process and analyze streaming data using standard SQL. The service enables you to quickly author and run powerful SQL code against streaming sources to perform time series analytics, feed real-time dashboards, and create real-time metrics. To get started with Kinesis Data Analytics, you create a Kinesis data analytics application that continuously reads and processes streaming data. The service supports ingesting data from Amazon Kinesis Data Streams and Amazon Kinesis Data Firehose streaming sources. https://docs.aws.amazon.com/kinesisanalytics/latest/dev/what-is.html

....do analytics in order to deliver near-real-time suggestions.. real time analysis means KDA...

Key: --> real time analytics --> fleet of ec2 instances collecting data & xfering it to RDS --> Another fleet monitoring and running sql queries to generate suggestions Expectation: Decouple + data analysts to run sql for analytics + durability Distractors: A/C/D A in the first look sounds good but Athena runs on data stored in S3 so the output returned will not be real time as Athena runs on files/objects present in the S3 C is a distractor cause it is queuing service which means it cannot render real time & question is not talking about slowness with ec2 instances and also no details about analytics D is distractor as SNS is push data service it does not gaurantee of message delivery and is not of course no solution to capture real time data B is correct because Kinesis data streams capture real time data in SHARDS which can be redirected to Data Analytics for querying the incoming data which comes at lightning speed and then use firehose to persist the data in s3

"SQL queries to provide recommendations" --> sessionization take a few minutes and read the below article which provides all about this. https://aws.amazon.com/blogs/big-data/create-real-time-clickstream-sessions-and-run-analytics-with-amazon-kinesis-data-analytics-aws-glue-and-amazon-athena/

B as analytics needs to deliver near-real-time suggestions.

Answer is B https://aws.amazon.com/blogs/big-data/writing-sql-on-streaming-data-with-amazon-kinesis-analytics-part-1/#:~:text=With%20the%20launch%20of%20Amazon,stream%20processing%20application%20in%20minutes.

Athena is for batch analysis, need leverage Kinesis Data Analytics for near-realtime analysis

IMO: A because it avoids sessionization using querys for other than data analysis (e.g. user event tracking): https://aws.amazon.com/blogs/big-data/create-real-time-clickstream-sessions-and-run-analytics-with-amazon-kinesis-data-analytics-aws-glue-and-amazon-athena/

A seem the correct answer.

B. Kinesis data analytics can use SQL statements

Decouple doesnt always point to SQS. This is a good example. give 10 mins to watch this video tutorial which will give you more info. https://www.youtube.com/watch?v=DPT3swb6zgI