A machinery company wants to collect data from sensors. A data analytics specialist needs to implement a solution that aggregates the data in near-real time and saves the data to a persistent data…

Question

Options

• AUse Amazon Kinesis Data Streams to receive the data from the sensors. Use Amazon Kinesis Data
• BUse Amazon Kinesis Data Firehose to receive the data from the sensors. Use Amazon Kinesis Data
• CUse Amazon Kinesis Data Firehose to receive the data from the sensors. Use an AWS Lambda function
• DUse Amazon Kinesis Data Firehose to receive the data from the sensors. Use an AWS Lambda function

• A
  73% (30)
• B
  7% (3)
• C
  17% (7)
• D
  2% (1)

Explanation

The two critical constraints are: nested JSON storage format and single-digit millisecond query latency. Amazon DynamoDB natively supports nested JSON documents and is specifically designed to deliver single-digit millisecond read/write latency at any scale. Option A uses Kinesis Data Streams for real-time ingestion and stream processing, then writes to DynamoDB - satisfying both constraints. Options B, C, and D use Kinesis Data Firehose, which is designed for high-throughput delivery to destinations like S3, Redshift, or OpenSearch. S3 does not offer single-digit millisecond query latency. Firehose also lacks real-time aggregation capability. Kinesis Data Streams, unlike Firehose, supports custom consumers for real-time aggregation before persistence.

No community discussion yet for this question.