Are relational databases like SQL Server ideal for managing high-speed, time-series plant data?

Relational databases like SQL Server are generally not ideal for managing high-speed, time-series plant data due to several inherent limitations in their design. Time-series data is characterized by large volumes of data points collected at very high frequencies, which can lead to challenges in performance and data management.

One of the main issues with relational databases is that they are optimized for structured, transactional queries and typically lack the efficiency needed for handling the large-scale, continuous data ingestion that time-series data requires. As the volume of incoming data increases, relational databases can experience performance degradation, causing slower query responses and potential bottlenecks.

Additionally, time-series data often incorporates specific storage and indexing needs, such as the ability to quickly aggregate and analyze data across time intervals. Relational databases, with their row-based storage model, may not provide the same efficiency as specialized time-series databases designed with a focus on these requirements.

Thus, while relational databases have their strengths, they are not well-suited for the unique characteristics and demands of high-speed, time-series data management.