DEA-C01 Question 155
Single answerA company needs to store large volumes of semi-structured data generated by an IoT fleet of sensors. The data needs to be queried with low-latency analytics and integrated with machine learning workflows. Which data store would be the most appropriate to meet these requirements?
- A
Amazon RDS
- B
Amazon Redshift
- C
Amazon DynamoDB
- D
Amazon S3 with Amazon Athena
Show answer and explanation
Correct answer: D
Explanation
Amazon S3 with Amazon Athena is the most appropriate choice for storing and analyzing large volumes of semi-structured IoT sensor data. S3 provides scalable and cost-effective storage, and Athena enables low-latency SQL-based querying on the data without requiring a traditional database. This combination also supports seamless integration with machine learning workflows, making it well-suited for the described requirements.
- A. Incorrect.
Amazon RDS is a relational database service that is best suited for structured transactional datasets rather than semi-structured data and is not optimized for analytics or machine learning workflows.
- B. Incorrect.
Amazon Redshift is a data warehouse designed for structured data and OLAP (Online Analytical Processing) workloads, but it is not ideal for querying semi-structured data such as JSON or IoT sensor data.
- C. Incorrect.
Amazon DynamoDB is a NoSQL database suitable for key-value and document-based workloads, but it does not provide native query capabilities for analytics or integration with machine learning workflows.
- D. Correct.
Amazon S3 with Amazon Athena is ideal for storing semi-structured data in a cost-effective manner (S3) and allows querying the data using SQL-based queries (Athena). Additionally, the data stored in S3 can easily be integrated with machine learning workflows using services like Amazon SageMaker.