DEA-C01 Question 146
Single answerYou are designing a data processing pipeline to analyze e-commerce transaction data. The data must be stored in a way that supports ad-hoc SQL queries with low-latency responses. Additionally, the data must scale efficiently as the e-commerce platform grows. Which storage solution should you choose?
- A
Amazon RDS with MySQL
- B
Amazon Redshift
- C
Amazon DynamoDB
- D
Amazon S3 with data stored in Parquet format
Show answer and explanation
Correct answer: B
Explanation
Amazon Redshift is the best choice for this scenario because it is specifically designed to handle analytical workloads with low-latency SQL queries on large datasets. It is highly scalable and integrates well with other AWS services, making it ideal for processing and analyzing e-commerce transaction data in a growing environment.
- A. Incorrect.
Amazon RDS with MySQL is a relational database service suitable for OLTP (Online Transaction Processing) workloads rather than large-scale analytical queries. It is not optimized for low-latency ad-hoc queries over large datasets.
- B. Correct.
Amazon Redshift is a fully managed data warehouse service designed for analytics and ad-hoc SQL queries over large-scale datasets. It provides low-latency query performance and scales efficiently for growing workloads, making it the ideal solution in this scenario.
- C. Incorrect.
Amazon DynamoDB is a NoSQL database service optimized for key-value and document-based workloads, not SQL-based analytical queries. It is not suitable for running complex ad-hoc SQL queries.
- D. Incorrect.
Amazon S3 with data stored in Parquet format is a cost-effective solution for storing large datasets. However, it lacks low-latency query performance for ad-hoc SQL queries unless paired with query engines like Amazon Athena, which can still introduce latency compared to a data warehouse like Amazon Redshift.