AWS Certified Solutions Architect Associate SAA-C03 Practice Question
A company is collecting large volumes of log data from their fleet of delivery vehicles, which includes timestamps, location coordinates, and sensor readings. This data needs to be analyzed to identify trends over time and optimize delivery routes. The most cost-effective AWS database type to support this use case with the primary focus on analytics over vast time-series data would be:
Amazon Timestream is a fast, scalable, and serverless time series database service for IoT and operational applications that makes it easy to store and analyze trillions of events per day at 1/10th the cost of relational databases. It is specifically designed for time series data, making it the most cost-effective choice for the described scenario. Amazon DynamoDB, while capable of handling time series data, is not specialized for such workloads and could be less cost-effective at scale for time-series analytics. Amazon RDS is a relational database service better suited for transactional data, not optimized for time series analytics. Amazon Redshift is a columnar database optimized for complex queries on structured data, but it is not specifically designed for time series data and can be more expensive for this type of workload.
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