DEA-C01 Question 260
Single answerYou are designing a data pipeline for a retail company to process and analyze customer transactions in near real-time. The pipeline is required to ingest streaming data, process it with low latency, and store it in a data lake for further analysis. Which AWS service combination is the most appropriate to meet these requirements?
- A
Amazon Kinesis Data Streams, AWS Lambda, and Amazon S3
- B
Amazon SQS, AWS Glue, and Amazon RDS
- C
Amazon Kinesis Data Firehose, Amazon EMR, and Amazon Redshift
- D
Amazon SNS, AWS Step Functions, and Amazon DynamoDB
Show answer and explanation
Correct answer: A
Explanation
To meet the requirements of ingesting, processing, and storing near real-time streaming data for analysis, Amazon Kinesis Data Streams efficiently handles streaming data ingestion, AWS Lambda processes data with low latency, and Amazon S3 provides a scalable data lake solution suitable for downstream analytical tasks. The other options involve services that are not fully optimized for this use case.
- A. Correct.
This is the correct combination. Amazon Kinesis Data Streams is ideal for ingesting streaming data, AWS Lambda can process data in near real-time with low latency, and Amazon S3 is a scalable and cost-effective storage solution for a data lake.
- B. Incorrect.
Amazon SQS is designed for message queuing and not optimized for real-time streaming. AWS Glue is more suited to ETL jobs and batch processing, while Amazon RDS is used for relational databases but not for data lakes.
- C. Incorrect.
Amazon Kinesis Data Firehose is designed for streaming data delivery, but Amazon EMR is better suited for big data processing and not low-latency real-time processing. Amazon Redshift is a data warehouse and not a data lake solution.
- D. Incorrect.
Amazon SNS is a notification service and not suitable for streaming data ingestion. AWS Step Functions is for orchestrating workflows, and Amazon DynamoDB is a NoSQL database, not a data lake.