DEA-C01 Question 82
Select 3A company is building a real-time data processing pipeline using AWS services. They need to collect streaming data from IoT devices, process it in near real-time, and store the processed data for analysis. The solution must ensure low latency and scalability. Which combination of AWS services should they use to meet these requirements?
- A
Amazon Kinesis Data Streams for ingesting streaming data
- B
AWS Lambda for processing data in near real-time
- C
Amazon RDS for storing the processed data
- D
Amazon DynamoDB for storing the processed data
- E
Amazon S3 for ingesting streaming data
Show answer and explanation
Correct answers: A, B, D
Explanation
To build a scalable and low-latency real-time data processing pipeline, Amazon Kinesis Data Streams is ideal for ingesting streaming data, AWS Lambda can process the data in real-time, and Amazon DynamoDB is well-suited for storing processed data due to its scalability and high throughput. This combination ensures the solution meets the company's requirements.
- A. Correct.
Amazon Kinesis Data Streams is specifically designed for real-time streaming data ingestion, making it an appropriate choice for collecting data from IoT devices.
- B. Correct.
AWS Lambda can be used to process data in near real-time as it can be triggered by events from Amazon Kinesis Data Streams, ensuring low latency.
- C. Incorrect.
Amazon RDS is not suitable for storing high-velocity data from IoT devices due to its relational nature and limited scalability compared to DynamoDB.
- D. Correct.
Amazon DynamoDB is a highly scalable NoSQL database that can efficiently handle high-velocity data storage, making it a good fit for storing processed IoT data.
- E. Incorrect.
Amazon S3 is a scalable object storage service but is not designed for ingesting streaming data in real-time. It is better suited for storing large amounts of data after processing.