Question #624
A logistics company tracks its fleet vehicles using GPS devices that send location data every 30 seconds to a central system. The system must store vehicle ID and timestamp for each reading. The company requires real-time access to current locations and historical routes, with the ability to scale seamlessly as the fleet expands. The solution must maintain high performance without downtime during scaling.
Which solution will meet these requirements MOST cost-effectively?
Store the GPS data in an Amazon RDS database. Create an index on the vehicle ID and timestamp columns. Use the columns to filter queries for real-time and historical data.
Store the GPS data in an Amazon DynamoDB table. Create a composite key using the vehicle ID and timestamp columns. Use the columns to filter queries for real-time and historical data.
Store the GPS data in Amazon ElastiCache for Redis. Create a SortedSet key using the vehicle ID and timestamp columns. Use the columns to filter queries for real-time and historical data.
Store the GPS data in Amazon S3. Partition the data by vehicle ID and timestamp columns. Use Amazon Athena to query historical data and Lambda for real-time access.
Explanation
The correct answer is B. Amazon DynamoDB is a fully managed NoSQL database designed for high scalability and low-latency access. Using a composite key (Vehicle ID as partition key and timestamp as sort key) enables efficient queries for real-time (latest entry) and historical data (range queries). DynamoDB auto-scales without downtime, handling frequent writes (every 30 seconds) cost-effectively.
Other options:
- A: RDS struggles with high write throughput and scaling requires downtime.
- C: ElastiCache (Redis) is in-memory, costly for large historical data, and less durable.
- D: S3 + Athena lacks real-time capabilities and introduces latency.
Key points: Prioritize managed services with auto-scaling, low-latency reads/writes, and efficient query patterns for time-series data.
Answer
The correct answer is: B