Exam AWS Certified Data Analytics - Specialty topic 1 question 50 discussion

B? https://aws.amazon.com/blogs/big-data/perform-near-real-time-analytics-on-streaming-data-with-amazon-kinesis-and-amazon-elasticsearch-service/

When Not to Use the KPL : The KPL can incur an additional processing delay of up to RecordMaxBufferedTime within the library (user-configurable). Larger values of RecordMaxBufferedTime results in higher packing efficiencies and better performance. Applications that cannot tolerate this additional delay may need to use the AWS SDK directly. For more information about using the AWS SDK with Kinesis Data Streams, see Developing Producers Using the Amazon Kinesis Data Streams API with the AWS SDK for Java. For more information about RecordMaxBufferedTime and other user-configurable properties of the KPL, see Configuring the Kinesis Producer Library. https://docs.aws.amazon.com/streams/latest/dev/developing-producers-with-kpl.html

B: I passed the test

Correct answer is B. Using PutRecord/PutRecords would send the data synchronously to Kinesis Data Streams. Kinesis Data Analytics can be detect anomalies and the data can be pushed to Kinesis Data Firehose with transformation to Elasticsearch for analysis. Option A is wrong as Kinesis data streams does not provide data transformation feature. Option C is wrong as copying data to S3 and loading to Redshift would not make it near-real time. Option D is wrong as using Glue is ideal for batch jobs and not for near-real time analytics.

Low latency retrival can be achieved with DynamoDB, Redis and OpenSearch. I guess that would be enough to select the answer.

Answer - B

near realtime should be opensearch

Answer is B

Answer - B

B - near-realtime analytics keyword, only ES can provide that from the set of options

B it is

Answer B

Looks like Answer is B.. https://docs.aws.amazon.com/streams/latest/dev/developing-producers-with-kpl.html When Not to Use the KPL The KPL can incur an additional processing delay of up to RecordMaxBufferedTime within the library (user-configurable). Larger values of RecordMaxBufferedTime results in higher packing efficiencies and better performance. Applications that cannot tolerate this additional delay may need to use the AWS SDK directly.

B is the answer

B is the right answer

The only thing about B is why transform to CSV since putting to Elastic Search ?

Answer B A and C dropped because RedShift is not meant for "near-real-time" analysis. Also, it would require some kind on Visualization on top of it to do the analysis. Dropped D because KDS in itself cannot transform data.