DEA-C01 Question 483
Select 3You are designing a data pipeline on AWS to process large-scale IoT sensor data in near real-time. The data is ingested continuously, and it needs to be transformed, enriched, and stored for analytics. Which combination of AWS services should you use to achieve this?
- A
Amazon Kinesis Data Streams for ingestion and AWS Lambda for real-time processing
- B
Amazon S3 for ingestion and Amazon EMR for real-time processing
- C
Amazon Kinesis Data Firehose for ingestion and transformation
- D
Amazon Redshift for ingestion and real-time data enrichment
- E
Amazon DynamoDB for storing enriched data
Show answer and explanation
Correct answers: A, C, E
Explanation
In this scenario, Amazon Kinesis Data Streams and Kinesis Data Firehose handle the ingestion of large-scale streaming data and its transformation. AWS Lambda can process the data in near real-time, while Amazon DynamoDB serves as a fast and scalable database for storing the enriched data. This combination of services ensures that the pipeline can handle real-time processing and storage requirements effectively.
- A. Correct.
Amazon Kinesis Data Streams is ideal for ingesting large-scale streaming data, and AWS Lambda can process data in near real-time, making this a suitable combination for the ingestion and processing step.
- B. Incorrect.
Amazon S3 is not designed for real-time ingestion, as it is optimized for batch processing and object storage, not for streaming data.
- C. Correct.
Amazon Kinesis Data Firehose supports ingestion and transformation of streaming data, making it suitable for this scenario.
- D. Incorrect.
Amazon Redshift is used for analytics and querying structured data at scale, but it is not suitable for direct ingestion or real-time enrichment of streaming data.
- E. Correct.
Amazon DynamoDB is a NoSQL database that is well-suited for storing enriched data, especially if low-latency access is required for further processing or applications.