DEA-C01 Question 81
Select 3You are tasked with designing a data pipeline that processes high-volume, unstructured data in real-time for a sentiment analysis application. The processed data should be stored in a data warehouse for analytical queries. Which combination of AWS services would best meet these requirements?
- A
Amazon Kinesis Data Streams for real-time ingestion and Amazon Redshift for analytical queries
- B
AWS Glue for real-time data transformation and Amazon S3 for storage
- C
Amazon Kinesis Data Firehose for real-time data delivery and Amazon Redshift for analytical queries
- D
Amazon EMR for real-time processing and Amazon DynamoDB for storage
- E
Amazon Kinesis Data Analytics for processing streams and Amazon Redshift for analytical queries
Show answer and explanation
Correct answers: A, C, E
Explanation
The best combination of AWS services for this scenario involves using real-time data ingestion and processing services (Amazon Kinesis Data Streams, Kinesis Data Firehose, and Kinesis Data Analytics) along with Amazon Redshift for storing and running analytical queries on the processed data. AWS Glue and EMR are not suitable for this real-time use case, while DynamoDB is not designed for complex analytical queries.
- A. Correct.
This is a correct option because Amazon Kinesis Data Streams can handle real-time ingestion of high-volume, unstructured data, and Amazon Redshift is a suitable choice for analytical queries.
- B. Incorrect.
AWS Glue is not optimized for real-time data transformation; it is typically used for ETL (Extract, Transform, Load) jobs on batch data, making this option unsuitable for real-time processing.
- C. Correct.
This is a correct option because Amazon Kinesis Data Firehose can deliver real-time streaming data directly into Amazon Redshift, enabling near real-time analytics.
- D. Incorrect.
Amazon EMR is more suited for distributed big data processing using frameworks like Hadoop or Spark, which is less ideal for real-time processing. Additionally, Amazon DynamoDB is a NoSQL database and not designed for analytical queries.
- E. Correct.
This is a correct option because Amazon Kinesis Data Analytics can process streaming data in real-time, and Amazon Redshift is well-suited for analytical queries on the processed data.