DEA-C01 Question 59
Select 2Your organization processes data from multiple sources, including IoT sensor streams (high velocity), daily transactional data from a relational database (structured data), and images uploaded by users (unstructured data). You need to design a scalable, cost-effective architecture on AWS to handle the volume, velocity, and variety of this data for downstream analytics. Which combination of services should you use?
- A
Amazon Kinesis Data Streams for ingesting IoT sensor streams, Amazon S3 for storing the images, and Amazon RDS for transactional data
- B
Amazon Redshift for all data ingestion and analytics processing
- C
Amazon Kinesis Data Firehose for IoT sensor streams, Amazon S3 for images, and Amazon Aurora for transactional data
- D
AWS Glue for data integration, Amazon EMR for processing, and Amazon DynamoDB for storing all types of data
- E
Amazon S3 for storing both structured and unstructured data, with AWS Lambda for ingestion and processing
Show answer and explanation
Correct answers: A, C
Explanation
The correct answers involve choosing services that align with the specific characteristics of the data: high velocity (IoT sensor streams), variety (structured and unstructured data), and volume. Amazon Kinesis Data Streams and Firehose are both reliable for handling high-velocity streaming data. Amazon S3 is the most cost-effective and scalable option for storing unstructured data such as images. For structured transactional data, relational databases like Amazon RDS or Amazon Aurora are the appropriate choices.
- A. Correct.
This is a valid solution. Amazon Kinesis Data Streams is ideal for high-velocity IoT data ingestion, Amazon S3 is cost-effective for storing unstructured data like images, and Amazon RDS is a managed relational database suitable for processing structured transactional data.
- B. Incorrect.
This is not correct. Amazon Redshift is a data warehouse optimized for analytical queries, not for handling high-velocity streams or unstructured data.
- C. Correct.
This is a valid solution. Amazon Kinesis Data Firehose can handle high-velocity data streams and deliver them to storage destinations like Amazon S3, which is also ideal for unstructured data such as images. Amazon Aurora is a scalable relational database suited for transactional data.
- D. Incorrect.
This is not correct. AWS Glue and Amazon EMR are excellent for processing and integration but are not designed for real-time ingestion or handling transactional workloads. Amazon DynamoDB is not an appropriate choice for storing unstructured data like images.
- E. Incorrect.
This is not fully correct. While Amazon S3 is a good choice for unstructured data, it is not designed to handle structured transactional data natively. AWS Lambda can process data but is not optimized for high-velocity streams.