DEA-C01 Question 152
Select 2Your company is building a real-time analytics platform to process millions of IoT sensor events per second. The processed data needs to be stored in a cost-effective manner for long-term analysis using SQL queries and machine learning. Which AWS data store(s) would be the most appropriate choice for this use case?
- A
Amazon S3 with Amazon Athena
- B
Amazon DynamoDB
- C
Amazon Redshift
- D
Amazon RDS
- E
Amazon ElastiCache
Show answer and explanation
Correct answers: A, C
Explanation
For real-time IoT data processing and long-term analytics, Amazon S3 with Athena provides a highly cost-effective option for storing data and running SQL queries. Amazon Redshift complements this by offering a highly optimized environment for complex analytics and machine learning. DynamoDB, RDS, and ElastiCache are either unsuitable for analytics or not designed for cost-effective, large-scale data storage.
- A. Correct.
Amazon S3 with Amazon Athena is a cost-effective solution for storing large amounts of unstructured or semi-structured data, and it supports SQL queries for analysis. This makes it suitable for long-term analytics and machine learning use cases.
- B. Incorrect.
Amazon DynamoDB is a NoSQL database designed for low-latency and high-availability use cases, such as web applications. It is not optimized for large-scale analytics or SQL-based queries.
- C. Correct.
Amazon Redshift is a data warehouse optimized for running complex SQL queries and analytics on structured data. It is well-suited for long-term analysis and machine learning workloads.
- D. Incorrect.
Amazon RDS is a managed relational database service suitable for transactional workloads. While it supports SQL queries, it is not the most cost-effective or scalable option for processing large-scale analytics data.
- E. Incorrect.
Amazon ElastiCache is designed for in-memory caching and low-latency data access. It is not suitable for long-term data storage or analytics.