Which type of data store is used to archive real-time observational data?

The spatiotemporal big data store is specifically designed to handle large volumes of real-time observational data, such as from sensors and other data collection methods that capture dynamic information over time and space. This type of data store can efficiently manage the complexity and scale of this data, allowing for both storage and querying of time-based and location-based information.

This data store enables users to perform real-time analytics and visualize trends over time, which is essential for applications in fields like environmental monitoring, urban planning, and transportation. It is optimized for handling vast datasets that change frequently, supporting temporal queries and spatial operations that are crucial for analyzing and interpreting observational data.

In contrast, other data stores, such as relational data stores, are better suited for structured data with fixed schemas and do not perform as well with the scale or the temporal and spatial characteristics that spatiotemporal big data stores provide. Tile cache data stores are focused on serving cached map tiles rather than managing observational data in real time. General data stores can refer to various data storage solutions, but lack the specific optimizations needed for real-time spatiotemporal data.