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A company is developing a new application that will process high volumes of telemetry data from IoT devices. The data is time-series in nature, requiring fast writes and efficient querying across time ranges and device IDs. The solution must support millions of data points per second and scale globally. Which Azure Cosmos DB API is the MOST appropriate for this scenario?
- AGremlin API
- BSQL (Core) API
- CMongoDB API
- DCassandra API
Show answer & explanationAnswer & explanation
Correct answer: D. Cassandra API
The Cassandra API is specifically optimized for high-throughput, time-series data, and is well-suited for IoT telemetry due to its distributed nature and efficient write/read patterns for this type of workload. Its column-family model aligns well with structured time-series data.
Why the other options are wrong
- A. The Gremlin API is designed for graph databases, which is not the primary use case for high-volume IoT telemetry data.
- B. While versatile, the SQL API might require more careful partitioning and indexing design to achieve the same write/read performance for extremely high-volume time-series data as Cassandra.
- C. The MongoDB API is good for document-oriented data but might not be as performant as Cassandra for specific time-series patterns at extreme scale.
Cosmos DB Cassandra API
The Azure Cosmos DB Cassandra API provides a highly scalable, globally distributed, and low-latency database service for Apache Cassandra applications.
- Ideal for time-series, IoT, and archival data.
- Supports Cassandra Query Language (CQL).
- Offers high throughput and low latency for large datasets.
Memory trick: Choosing the right Cosmos DB API is like picking the perfect tool from a specialized toolbox.