DAS-C01 · Question #132
A manufacturing company wants to create an operational analytics dashboard to visualize metrics from equipment in near-real time. The company uses Amazon Kinesis Data Streams to stream the data to…
The correct answer is C. Use Amazon Kinesis Data Firehose to push the data into an Amazon Elasticsearch Service. The requirement is near-real-time visualization with a 5-second refresh and least implementation effort. Option C uses Amazon Kinesis Data Firehose to push data into Amazon Elasticsearch Service (now OpenSearch Service), then uses Kibana - which is natively bundled with…
Question
A manufacturing company wants to create an operational analytics dashboard to visualize metrics from equipment in near-real time. The company uses Amazon Kinesis Data Streams to stream the data to other applications. The dashboard must automatically refresh every 5 seconds. A data analytics specialist must design a solution that requires the least possible implementation effort. Which solution meets these requirements?
Options
- AUse Amazon Kinesis Data Firehose to store the data in Amazon S3. Use Amazon QuickSight to
- BUse Apache Spark Streaming on Amazon EMR to read the data in near-real time. Develop a
- CUse Amazon Kinesis Data Firehose to push the data into an Amazon Elasticsearch Service
- DUse AWS Glue streaming ETL to store the data in Amazon S3. Use Amazon QuickSight to build
How the community answered
(21 responses)- A5% (1)
- B5% (1)
- C76% (16)
- D14% (3)
Explanation
The requirement is near-real-time visualization with a 5-second refresh and least implementation effort. Option C uses Amazon Kinesis Data Firehose to push data into Amazon Elasticsearch Service (now OpenSearch Service), then uses Kibana - which is natively bundled with OpenSearch - as the dashboard. Kibana has a built-in auto-refresh feature that supports intervals as low as 5 seconds, requiring no custom development. Option A and D rely on QuickSight, which does not natively support sub-minute auto-refresh. Option B requires standing up an EMR cluster and custom Spark Streaming code, significantly increasing implementation effort.
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