Low-Latency IoT Data Ingestion with Kinesis and DynamoDB
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?
Community Votes
100% of anonymous learners picked answer C. Votes are pick records left by other test-takers — they are not the verified answer.
Community Insight
The core trap is selecting managed storage that supports nested JSON and sub-10ms latency without managing servers, which points directly to DynamoDB over S3 or RDS.
This question evaluates the selection of AWS services for real-time sensor data ingestion and low-latency querying. The correct solution leverages Amazon Kinesis Data Streams for ingestion and Amazon DynamoDB for storage to minimize operational overhead.
Learners often choose Amazon S3 (Option A or B) because it is a common data lake store, but S3 does not provide single-digit millisecond query latency natively without additional processing layers like Athena or Redshift Spectrum.
Community Discussion (5 comments)
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Expert Analysis
Why the Answer Is Correct
Option C is the only solution that satisfies all constraints: near-real-time ingestion, nested JSON support, <10ms query latency, and least operational overhead. Amazon Kinesis Data Streams is a fully managed service designed for continuous data streaming from sources like IoT sensors. Amazon DynamoDB is a NoSQL database that natively stores document-like structures (nested JSON) and offers predictable single-digit millisecond performance at any scale.Why the Other Options Are Wrong
Option A suggests using a self-hosted Apache Kafka cluster, which introduces significant operational overhead for patching, scaling, and maintenance, violating the 'least operational overhead' requirement. Option B uses Lambda for processing but stores in S3; S3 is an object store optimized for throughput, not low-latency random access queries. Option D uses SQS and Glue, which are batch-oriented or message-buffering tools, not suitable for real-time, high-frequency sensor data ingestion with immediate low-latency query needs.Community Comment Notes
The community consensus strongly favors Option C. As user Snape noted, "near real time = Kinesis Data streams," highlighting the key ingestion mechanism. User Felix_G emphasized that DynamoDB provides "single digit millisecond" performance, which is critical for the <10ms requirement. Another commenter, rralucard_, confirmed that this combination is well-optimized for real-time ingestion and query performance.Exam Strategy
When a question specifies 'least operational overhead,' immediately eliminate self-managed solutions (like EC2-based Kafka) and batch-processing pipelines (like Glue). For 'single-digit millisecond' latency with JSON data, DynamoDB is the primary candidate.
Frequently Asked Questions
Why not use Amazon S3 for storing sensor data?
S3 is an object store optimized for large-scale data transfer, not low-latency queries. It does not provide single-digit millisecond response times for individual records.
Does DynamoDB support nested JSON format?
Yes, DynamoDB is a NoSQL database that natively supports document-like structures, including nested JSON attributes, making it ideal for complex sensor data.
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