Exam AWS Certified Solutions Architect - Associate SAA-C02 topic 1 question 6 discussion

The question asks for "the solution should PROCESS the STREAMING data with ...." Kinesis Data Streams - Collect live streaming data from Sources Kinesis Data Analytics - To Analyze and process the data (C) and (E) for me...

D. Amazon Kinesis Data Firehose E. Amazon Kinesis Data Analytics

For cost-effective, near-real-time data processing with minimal effort, the best solution is: 🔥 Kinesis Data Firehose + AWS Lambda 🔥 🚀 Why Not the Other Options? ❌ A. Amazon EC2 - High operational overhead—requires managing instances, scaling, and maintenance. - Not cost-effective for real-time streaming compared to serverless options like Lambda. ❌ C. Amazon Kinesis Data Streams - More complex than Kinesis Data Firehose—requires setting up a consumer application to process data. - Better suited for custom real-time analytics, whereas Firehose automatically integrates with Redshift. ❌ E. Amazon Kinesis Data Analytics - Best for real-time SQL-based analytics, but the question focuses on batch processing with Redshift. - Not needed if the goal is just to ingest data into Redshift quickly.

DE makes sense at this point, you capture real time data, then you analyse it in real time.

Correct answer is CE as kinesis data stream for streaming data near real team and to KDA is to analysis data works well with KDS

Batch to realtime is the primary use case for Kinesis. Option B, Lambda can be used for later processing but it is not the answer. Option C is therefore the correct answerhttps://aws.amazon.com/kinesis/

C and E

Kinesis Data Firehose:Data transfer service for loading streaming data into Amazon S3, Splunk, ElasticSearch, and RedShift. The primary purpose of Kinesis Firehose focuses on loading streaming data to Amazon S3, Splunk, ElasticSearch, and RedShift.

i think c and e

Kinesis Data Streams - Collecting live stream of data Kinesis Data Analytics - Analyzing and process the data

I'd say DE, because firehose fulfills both of the "near-real-time" and "low operational overhead" due to the fact that it's fully managed. Also E because we want timely insights on the data, and AWS lambda isn't used for that.

My answer is A

going with BD

Near real time: Amazon Kinesis Data Firehose is the easiest way to capture, transform, and load data streams into AWS data stores for near real-time analytics with existing business intelligence tools. Analysis: Amazon Kinesis Data Analytics is the easiest way to process data streams in real time with SQL or Apache Flink without having to learn new programming languages or processing frameworks.

What I understand from the question is that the data should be delivered to the Redshift, but in a near real time manner and not on a nightly basis, and also in a cost-effective way. The service that is able to deliver data to the Redshift in a real time manner is Firehose. The cheapest way to transform data when we are using Firehose is using Lambda. So, the answer should be B & D.

Not sure if (e) is needed, as our goal is to collect live streaming data from source -> Save to Redshift in near-real-time. I think the analyzing data part is not our concern, I think moving the data TO the Redshift is our concern. So I'm voting C, D

Question lists two functions. One is data import into redshift for analysts and second is real time streaming. With the combination of Firehose and KDA for receiving, real time processing and importing to Redhsift for later processing is feasible.