DEA-C01 Question 158
Select 3You are tasked with designing a data pipeline to process large volumes of streaming event data generated by IoT devices. The data needs to be ingested, transformed, and stored in a data warehouse for further analysis. Additionally, real-time analytics are required to monitor anomalies in the incoming data. Which AWS services should you use to build this pipeline?
- A
Amazon Kinesis Data Streams for ingesting streaming data
- B
AWS Glue for transforming the streaming data in real-time
- C
Amazon Redshift for storing processed data for analytical queries
- D
Amazon S3 for ingesting and storing raw streaming data
- E
Amazon Kinesis Data Analytics for performing real-time anomaly detection
Show answer and explanation
Correct answers: A, C, E
Explanation
To design a real-time data pipeline for IoT streaming data, you need Amazon Kinesis Data Streams to ingest the streaming data, Amazon Kinesis Data Analytics to perform real-time anomaly detection, and Amazon Redshift to store the processed data for further analysis. AWS Glue and Amazon S3 are not suitable for this specific scenario because Glue is designed for batch processing, and S3 cannot handle real-time data ingestion.
- A. Correct.
Correct: Amazon Kinesis Data Streams is designed to handle real-time ingestion of high volumes of streaming data, making it suitable for IoT event data.
- B. Incorrect.
Incorrect: AWS Glue is primarily used for batch ETL operations and not for real-time data transformation, which is required in this use case.
- C. Correct.
Correct: Amazon Redshift is a data warehouse service that supports complex analytical queries, making it ideal for storing processed data for further analysis.
- D. Incorrect.
Incorrect: Amazon S3 is an object storage service, not a streaming ingestion service. It is not ideal for real-time streaming data ingestion.
- E. Correct.
Correct: Amazon Kinesis Data Analytics enables real-time processing and analytics on streaming data, making it suitable for anomaly detection in this scenario.