Topic 1 Question 63
A manufacturing company wants to collect data from sensors. A data engineer needs to implement a solution that ingests sensor data in near real time. The solution must store the data to a persistent data store. The solution must store the data in nested JSON format. The company must have the ability to query from the data store with a latency of less than 10 milliseconds. Which solution will meet these requirements with the LEAST operational overhead?
Use a self-hosted Apache Kafka cluster to capture the sensor data. Store the data in Amazon S3 for querying.
Use AWS Lambda to process the sensor data. Store the data in Amazon S3 for querying.
Use Amazon Kinesis Data Streams to capture the sensor data. Store the data in Amazon DynamoDB for querying.
Use Amazon Simple Queue Service (Amazon SQS) to buffer incoming sensor data. Use AWS Glue to store the data in Amazon RDS for querying.
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コメント(5)
- 正解だと思う選択肢: C
Option C, using Amazon Kinesis Data Streams to capture the sensor data and storing it in Amazon DynamoDB for querying, is the best solution to meet the requirements with the least operational overhead. This solution is well-optimized for real-time data ingestion, supports the desired data format, and provides the necessary query performance.
👍 3rralucard_2024/02/02 Option C is the best solution to meet the requirements with the least operational overhead:
Use Amazon Kinesis Data Streams to ingest real-time sensor data Store the nested JSON data in Amazon DynamoDB for low latency queries The key advantages of Option C are:
Kinesis Data Streams fully manages real-time data ingestion with auto-scaling and persistence DynamoDB provides single digit millisecond latency for queries DynamoDB natively supports nested JSON data models Fully managed services minimize operational overhead In contrast:
Option A requires managing Kafka clusters Option B uses Lambda which can't provide persistent storage Option D requires integrating SQS, Glue, and RDS leading to complexity
👍 3Felix_G2024/03/02- 正解だと思う選択肢: C
near real time = Kinesis Data streams
👍 3Snape2024/05/01
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