CompTIA DataSys+ (DS0-001)Database DeploymentMedium
A database administrator is evaluating different storage options for a new database that will primarily store time-series data from IoT sensors. The data is written continuously in large volumes, and queries often involve aggregating data over specific time ranges. Which storage approach would be most efficient for this type of workload?
- AStandard relational database with B-tree indexes on timestamps
- BDocument database storing each sensor reading as a JSON document
- CKey-value store where each timestamp is a key and sensor reading is a value
- DTime-series database optimized for timestamped data ingestion and range queries
Show answer & explanationAnswer & explanation
Correct answer: D. Time-series database optimized for timestamped data ingestion and range queries
Time-series databases are purpose-built for handling high volumes of timestamped data, offering optimized storage, indexing, and query capabilities for time-based aggregations and range queries. This makes them significantly more efficient than general-purpose databases for IoT sensor data.
Why the other options are wrong
- A. While possible, a standard relational database would struggle with the ingestion rate and efficiency of time-based aggregations compared to a specialized time-series database.
- B. Document databases are flexible but not optimized for the specific characteristics of time-series data (continuous writes, time-range queries) and would likely be less efficient for aggregation.
- C. A key-value store would be inefficient for range queries and aggregations across many keys, as it's optimized for direct key lookups.
Time-Series Database (TSDB)
A database optimized for storing and retrieving time-stamped or time-series data, often used for monitoring, IoT, and financial data analytics.
- Optimized for high-volume data ingestion.
- Efficient storage and indexing for timestamped data.
- Specialized functions for time-based aggregations and range queries.
Memory trick: Match the database to its data's rhythm.