Exam AWS Certified Solutions Architect - Associate SAA-C03 topic 1 question 59 discussion

Option D is the most appropriate solution for transmitting and processing the clickstream data in this scenario. Amazon Kinesis Data Streams is a highly scalable and durable service that enables real-time processing of streaming data at a high volume and high rate. You can use Kinesis Data Streams to collect and process the clickstream data in real-time. Amazon Kinesis Data Firehose is a fully managed service that loads streaming data into data stores and analytics tools. You can use Kinesis Data Firehose to transmit the data from Kinesis Data Streams to an Amazon S3 data lake. Once the data is in the data lake, you can use Amazon Redshift to load the data and perform analysis on it. Amazon Redshift is a fully managed, petabyte-scale data warehouse service that allows you to quickly and efficiently analyze data using SQL and your existing business intelligence tools.

Option D. https://aws.amazon.com/es/blogs/big-data/real-time-analytics-with-amazon-redshift-streaming-ingestion/

Ans D - using Kinesis Streams / Firehouse (data in/out) is fast and reliable. Using Redshift allows all sorts of permutations of data analyses and interfacing to user apps

D is the best option

A: Not sure how recent this question is but Data Pipeline is not really a product AWS is recommending anymore https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/what-is-datapipeline.html B: 30TB of clickstream data could be done with EC2 but it would be challenging C: CloudFront is for CDN and caching and mostly outgoing data, not incoming. D: Kinesis, S3 data lake and Redshift will work perfectly for this case

The answer should be A. Clickstream does not mean real time, it just means they capture user interactions on the web page. Kinesis data streaming is not required. Furthermore, redshift is a data warehousing solution, it cant run complex analysis as well as EMR. My vote goes for A

when you see clickstream data, think about Kinesis Data Stream

The key reasons are: Kinesis Data Streams can continuously capture and ingest high volumes of clickstream data in real-time. This handles the large 30TB daily data intake. Kinesis Firehose can automatically load the streaming data into S3. This creates a data lake for further analysis. Firehose can transform and analyze the data in flight before loading to S3 using Lambda. This enables real-time processing. The data in S3 can be easily loaded into Amazon Redshift for interactive analysis at scale. Kinesis auto scales to handle the high data volumes. Minimal effort is needed for infrastructure management.

Option D is the correct answer

A. This option utilizes S3 for data storage and EMR for analytics, Data Pipeline is not ideal service for real-time streaming data ingestion and processing. It is better suited for batch processing scenarios. B. This option involves managing and scaling EC2, which adds operational overhead. It is also not real-time streaming solution. Additionally, use of Redshift for analyzing clickstream data might not be most efficient or cost-effective approach. C. CloudFront is CDN service and is not designed for real-time data processing or analytics. While using Lambda to process data can be an option, it may not be most efficient solution for processing large volumes of clickstream data. Therefore, collecting the data from Kinesis Data Streams, using Kinesis Data Firehose to transmit it to S3 data lake, and loading it into Redshift for analysis is the recommended approach. This combination provides scalable, real-time streaming solution with storage and analytics capabilities that can handle high volume of clickstream data.

Clickstream is the key - Answer is D

I am going to be unpopular here and I'll go for A). Even if here are other services that offer a better experience, data Pipeline can do the job here. "you can use AWS Data Pipeline to archive your web server's logs to Amazon Simple Storage Service (Amazon S3) each day and then run a weekly Amazon EMR (Amazon EMR) cluster over those logs to generate traffic reports" https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/what-is-datapipeline.html In the question there is no specific timing requirement for analytics. Also the EMR cluster job can be scheduled be executed daily. Option D) is a valid answer too, however with Amazon Redshift Streaming Ingestion "you can connect to Amazon Kinesis Data Streams data streams and pull data directly to Amazon Redshift without staging data in S3" https://aws.amazon.com/redshift/redshift-streaming-ingestion. So in this scenario Kinesis Data Firehose and S3 are redundant.

Option D

It is C. The image in here https://aws.amazon.com/kinesis/data-firehose/ shows how kinesis can send data collected to firehose who can send it to Redshift. It is also possible to use an intermediary S3 bucket between firehose and redshift. See image in here https://aws.amazon.com/blogs/big-data/stream-transform-and-analyze-xml-data-in-real-time-with-amazon-kinesis-aws-lambda-and-amazon-redshift/

Why not A? You can collect data with AWS Data Pipeline and then analyze it with EMR. Whats wrong with this option?

D is correct

Click Stream & Analyse/ process- Think KDS,