What improvement can adding nodes to the spatiotemporal big data store provide?

Adding nodes to a spatiotemporal big data store enhances its ability to handle larger volumes of data by distributing the workload across multiple nodes. This capability facilitates higher data write throughput, meaning that the system can ingest and process more data simultaneously compared to a scenario with fewer nodes.

As the infrastructure expands with additional nodes, each node can manage its own portion of the data, leading to parallel processing and improved efficiency in data writing operations. This is particularly beneficial for applications that deal with high-frequency data updates, such as those found in real-time environmental monitoring or large-scale urban planning, where quick analysis and response times are crucial.

While it may also contribute to lower latency or reduced backup time in some scenarios, the primary and most direct improvement from adding nodes relates specifically to the increased capacity and speed of data writes. Increased redundancy is a byproduct of this setup, enhancing the system's reliability but does not directly impact the performance in writing data.