Exam AWS Certified Solutions Architect - Associate SAA-C02 topic 1 question 194 discussion

I agree with C. DynamoDB provides high performance for retrieving data but is not used for analytics. Also, regarding the other comments, the question doesn't mention using DAX to improve the performance. On the other hand, Redshift is used for high performance OLAP processing, which is what is required here

Main words are: thousands of sensors, stream high volumes of data each second, solution that ingests and stores data, analyze data in near-real time, millisecond responsiveness. So solution ask for: 1) ingest data, 2) store data. Then solution also must be found IN THE WAY engineers can analyze data in near-real time with millisecond responsiveness. About ingestion no doubt about kinesis. About store data, redshift is a data warehousing service, not a data store service. Moreover lambda doesn’t work with redshift while it does with dynamodb. Analyze meaning is different from analytics, i do a query and analyze the response, no need of a datawarehouse. This together with millisecond responsiveness request, carry to dynamodb. D is correct.

Amazon Kinesis Data Streams: Kinesis Data Streams is designed to ingest and process large streams of data records in real time. It can scale to handle very high throughput, making it suitable for the high volumes of data generated by thousands of sensors. It provides real-time processing capabilities, ensuring that the data is available for analysis with minimal latency. AWS Lambda: AWS Lambda can be used to consume data from Kinesis Data Streams in real time. Lambda functions can process each data record as it arrives, enabling near-real-time analysis with millisecond responsiveness. Lambda automatically scales based on the incoming workload, ensuring that processing keeps up with the data ingestion rate. Amazon Redshift: Amazon Redshift is a fully managed data warehouse service that is optimized for analytics workloads. It can efficiently store and query large volumes of structured data.

C Here's why: Amazon Kinesis Data Streams: It is designed to ingest and process large streams of data records in real time. It can handle high volumes of data and provides millisecond responsiveness. Kinesis Data Streams can scale elastically based on the volume of incoming data. AWS Lambda: Lambda can be used to process the data from Kinesis Data Streams in near-real time. Lambda functions can be triggered automatically when new data arrives in the stream, allowing for immediate processing. Amazon Redshift: Redshift is a fully managed data warehouse service that is optimized for high-performance analysis of large datasets. It can handle complex analytics queries with millisecond response times, making it suitable for near-real-time analysis of the ingested data.

My answer is D. AWS Kinesis and DynamoDB are always very good combination for streaming-analysis purpose. It is easy to search for "Real Time Data Analytics with AWS Kinesis and DynamoDB".

changed to D after some research. Reshift is not for store bot to analysis. questions asks to store data, which can be achieved with Dynamodb

stores data = DynamoDB storage Redshift = data warehouse

*******Verified the Answer using Neal Davis *********** D

Amazon Redshift is ideal for online analytical processing (OLAP) using your existing business intelligence tools. Organizations are using Amazon Redshift to: Analyze global sales data for multiple products. Store historical stock trade data

When not to use DynamoDB: When multi-item or cross table transactions are required. When complex queries and joins are required. When real-time analytics on historic data is required.

Kinesis to stream data Lambda to process Dynamo to store (better to examine, lambda works better with dynamo, and it is operational purposes here) https://aws.amazon.com/blogs/big-data/detect-change-points-in-your-event-data-stream-using-amazon-kinesis-data-streams-amazon-dynamodb-and-aws-lambda/

C is the best answer - collect , process and examine(analyse)

Which is Intenet connected sensor always using MQTT, ZMQ and other. For Kinese need more program code, I don't understandt why not using SQS

D seems to be appropriate

Kinesis, Lambda and DynamoDB (storage)

KDS is real time not near time... Furthermore if an event is delivered to SQS, there is an automatic delay -- anywhere from 100 ms to several seconds -- before it is read. Answer is B

Read question D " stores the data in Amazon DynamoDB", DynamoDB is not a Datastore, Datastore is Redshift. I fell answer should be C