DEA-C01 Question 450
Select 3You are working as a Data Engineer for a retail company that processes millions of transactions daily. The company wants to build a near real-time analytics pipeline using AWS services. The data from their Point of Sale (POS) systems is streamed continuously, and they need to process, transform, and store the data for analytics. Which combination of AWS services would be most appropriate for building this solution?
- A
Amazon Kinesis Data Streams for ingesting streaming data
- B
AWS Lambda for processing and transforming the data
- C
Amazon Redshift for storing the raw streaming data
- D
Amazon S3 for storing transformed data in a data lake
- E
Amazon DynamoDB for running complex analytical queries
Show answer and explanation
Correct answers: A, B, D
Explanation
To build a near real-time analytics pipeline, you need a combination of services that can handle data ingestion, processing, and storage effectively. Amazon Kinesis Data Streams is ideal for ingesting streaming data, AWS Lambda can process and transform the data in real-time, and Amazon S3 provides a scalable and cost-effective storage solution for the transformed data. This setup ensures a robust and scalable pipeline tailored for real-time analytics.
- A. Correct.
Amazon Kinesis Data Streams is a service designed for ingesting streaming data in real-time, making it suitable for this scenario.
- B. Correct.
AWS Lambda is a serverless computing service that can process and transform the streaming data in real-time, fitting the requirements for near real-time processing.
- C. Incorrect.
Amazon Redshift is a data warehouse service optimized for analytical queries but is not ideal for storing raw streaming data. It is better suited for batch-loaded, structured data.
- D. Correct.
Amazon S3 is a cost-effective and scalable storage solution commonly used as a data lake for storing transformed data for analytics.
- E. Incorrect.
Amazon DynamoDB is a NoSQL database optimized for low-latency, high-throughput operations, but it is not designed for running complex analytical queries or storing large volumes of transformed data.