CompTIA DataSys+ (DS0-001)Database DeploymentMedium
A database administrator is planning to deploy a new database system that will primarily store and analyze time-series data from IoT sensors. The data arrives at a very high ingestion rate, and queries will mostly involve aggregations over time ranges (e.g., 'average temperature over the last hour'). Which database type is best suited for this specific workload?
- AGraph Database (e.g., Neo4j)
- BDocument Database (e.g., MongoDB)
- CRelational Database (e.g., PostgreSQL)
- DTime-Series Database (e.g., InfluxDB)
Show answer & explanationAnswer & explanation
Correct answer: D. Time-Series Database (e.g., InfluxDB)
Time-series databases are purpose-built for handling very high ingestion rates of timestamped data and are highly optimized for queries involving time-based aggregations and range scans, making them ideal for IoT sensor data.
Why the other options are wrong
- A. Graph databases are designed for data with complex relationships, not high-volume, time-ordered sensor data.
- B. Document databases are flexible for unstructured data but lack the specific optimizations for time-series workloads.
- C. While possible, relational databases are not optimized for the unique challenges of time-series data with high ingestion and time-based queries.
Time-Series Database (TSDB)
A Time-Series Database (TSDB) is a database optimized for storing and retrieving time-stamped data, such as sensor readings or financial market data, offering high ingestion rates and efficient time-based queries.
- Optimized for timestamped data.
- Handles very high write (ingestion) rates.
- Efficient for time-range queries and aggregations.
- Commonly used for IoT, monitoring, and financial data.
Memory trick: For 'time' and 'series' data, use the database that has 'time-series' in its name!