Is it true that the spatiotemporal big data store should be installed on its own machine?

The spatiotemporal big data store is designed to handle large volumes of spatial and temporal data efficiently, which requires significant system resources. Installing it on its own machine ensures that it can leverage the full capacity of hardware resources, such as memory, CPU, and disk I/O, without competition from other applications. This dedicated environment helps maintain optimal performance, reduces latency, and enhances the ability to scale up as data volumes grow.

When the spatiotemporal big data store is isolated on a separate server, it also simplifies management and maintenance tasks, allowing for targeted tuning and upgrades specific to that service without impacting other systems. Such a configuration is particularly important in high-demand environments or when dealing with complex spatial queries and analytics.

In contrast, sharing resources with other applications could lead to resource contention, resulting in degraded performance or system instability, especially when dealing with large datasets that require intensive processing. Thus, having the spatiotemporal big data store on its own machine is a best practice to ensure efficiency and reliability in data handling.