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 d

Using a self-hosted Apache Kafka cluster incurs significant operational overhead for management and scaling, which goes against the requirement for 'least operational overhead.'

Storing data in Amazon S3 for querying by AWS Lambda would not meet the requirement for query latency of less than 10 milliseconds, as S3 queries typically involve higher latency.

While Amazon Kinesis Data Streams and Amazon DynamoDB can capture and store data with low latency, Kinesis Data Streams requires more management overhead (e.g., shard configuration and scaling) compared to the fully serverless and automatically scaling SQS and Lambda combination.

Amazon SQS offers a highly scalable and durable messaging queue with minimal operational overhead, decoupling the ingestion of sensor data. AWS Lambda can then process messages from SQS in near real-time, transform them as needed, and efficiently store the nested JSON data into Amazon DynamoDB, which guarantees single-digit millisecond query latency and provides a persistent, serverless store. This combination provides a fully managed, scalable, and low-latency solution with very little operational management.