DEA-C01 Question 60
Select 3A company is building a data pipeline to handle a large volume of sensor data generated from IoT devices in real time. The data is semi-structured and needs to be analyzed for trends and anomalies as quickly as possible. Which AWS services should the company use to efficiently handle the volume, velocity, and variety of this data?
- A
Amazon Kinesis Data Streams for real-time data ingestion
- B
Amazon RDS for storing semi-structured data
- C
Amazon S3 for scalable object storage
- D
Amazon Redshift for querying structured data at scale
- E
AWS Glue for transforming and cataloging data
Show answer and explanation
Correct answers: A, C, E
Explanation
To handle the volume, velocity, and variety of IoT sensor data, Amazon Kinesis Data Streams is used for real-time ingestion, Amazon S3 is used for scalable storage of semi-structured data, and AWS Glue is used for transforming and cataloging the data. Together, these services address the challenges of managing IoT data in AWS.
- A. Correct.
Amazon Kinesis Data Streams is designed for handling high-velocity, real-time data ingestion, making it suitable for IoT sensor data.
- B. Incorrect.
Amazon RDS is a relational database service suited for structured data, not semi-structured data, and may not handle the velocity of real-time IoT data efficiently.
- C. Correct.
Amazon S3 provides a scalable and cost-effective storage solution for the large volume of semi-structured IoT data.
- D. Incorrect.
Amazon Redshift is optimized for structured data and analytical queries, but it is not ideal for storing semi-structured data or handling real-time ingestion.
- E. Correct.
AWS Glue can be used to transform and catalog the semi-structured data, making it easier to analyze and integrate with other services.