DEA-C01 Question 538
Select 2You are tasked with designing a data pipeline for processing large-scale real-time streaming data from IoT devices using AWS services. The data must be ingested, processed, and stored for further analysis. You need to ensure high availability, scalability, and minimal operational overhead. Which combination of AWS services should you use to meet these requirements?
- A
Amazon Kinesis Data Streams for data ingestion, AWS Lambda for processing, and Amazon S3 for storage
- B
Amazon EC2 instances for data ingestion and processing, and Amazon RDS for storage
- C
Amazon Kinesis Data Firehose for data ingestion and storage, and Amazon Redshift for data analysis
- D
AWS IoT Core for data ingestion, Amazon Kinesis Data Analytics for processing, and Amazon DynamoDB for storage
- E
Amazon SQS for data ingestion, Amazon EMR for processing, and Amazon Elasticsearch Service for storage
Show answer and explanation
Correct answers: A, D
Explanation
To design a real-time data pipeline for IoT devices, you need services that support real-time data ingestion, processing, and storage. Amazon Kinesis Data Streams and AWS IoT Core are great for ingestion. AWS Lambda and Amazon Kinesis Data Analytics handle real-time processing effectively. For storage, Amazon S3 and DynamoDB provide scalability and minimal operational overhead. These combinations ensure high availability, scalability, and easy management of large-scale streaming data.
- A. Correct.
Correct: Amazon Kinesis Data Streams is a fully managed service for ingesting real-time streaming data. AWS Lambda provides serverless processing, and Amazon S3 offers scalable storage with minimal operational overhead.
- B. Incorrect.
Incorrect: Using Amazon EC2 for ingestion and processing increases operational overhead due to the need to manage instances. Amazon RDS is not ideal for storing large-scale streaming data.
- C. Incorrect.
Partially correct: Amazon Kinesis Data Firehose is suitable for real-time data ingestion and storage. However, Amazon Redshift is a data warehouse primarily designed for querying structured data and not optimized for real-time storage.
- D. Correct.
Correct: AWS IoT Core is designed for ingesting IoT device data. Amazon Kinesis Data Analytics provides real-time data processing, and Amazon DynamoDB is a scalable, low-latency storage solution.
- E. Incorrect.
Incorrect: While Amazon SQS is a message queue service, it is not ideal for real-time data ingestion. Amazon EMR is suitable for batch processing, and Amazon Elasticsearch Service is used for search and analytics, not primary data storage.