DEA-C01 Question 65
Select 2You are designing a distributed data processing pipeline for a large financial organization. The system must handle petabytes of data daily with low latency and provide fault tolerance. Which AWS services and features would you use to meet these requirements?
- A
Amazon S3 for scalable, durable storage and Amazon EMR for distributed processing
- B
Amazon RDS for relational data storage and AWS Lambda for event-driven execution
- C
Amazon DynamoDB for NoSQL storage and AWS Glue for data cataloging and ETL
- D
Amazon Kinesis Data Streams for real-time data ingestion and Amazon EMR for distributed processing
- E
Amazon Redshift for data warehousing and Amazon EC2 for highly customized processing tasks
Show answer and explanation
Correct answers: A, D
Explanation
To meet the requirements of handling petabytes of data daily with low latency and fault tolerance, you need services that support scalable storage, distributed processing, and real-time data ingestion. Amazon S3 and Amazon EMR are ideal for durable storage and distributed processing. Additionally, Amazon Kinesis Data Streams and Amazon EMR are well-suited for real-time data ingestion and distributed processing. The other options either don't meet the scale requirements or are not designed for distributed compute workloads.
- A. Correct.
Amazon S3 provides highly durable and scalable storage, while Amazon EMR is designed for distributed data processing, making it ideal for handling petabytes of data with fault tolerance.
- B. Incorrect.
Amazon RDS is suitable for relational database use cases but is not designed for large-scale distributed processing. AWS Lambda is event-driven and not optimized for handling petabytes of data in a distributed system.
- C. Incorrect.
Amazon DynamoDB is a NoSQL database suited for high-throughput, low-latency workloads, but it is not designed for large-scale distributed processing. AWS Glue is primarily used for ETL and data cataloging, not distributed compute.
- D. Correct.
Amazon Kinesis Data Streams is designed for real-time data ingestion at scale, and Amazon EMR provides distributed processing capabilities, making this combination suitable for the given requirements.
- E. Incorrect.
Amazon Redshift is a data warehousing service optimized for analytics, not real-time or distributed processing. Amazon EC2 can be used for custom processing but would lack the built-in distributed compute capabilities of EMR.