SQL often struggles when it comes to managing massive amounts of time series data, but it’s not because of the language itself. The main culprit is the architecture that SQL typically works in, namely relational databases, which quickly become inefficient because they’re not designed for analytical queries of large volumes of time series data.
Traditionally, SQL is used with relational database management systems (RDBMS) that are inherently transactional. They are structured around the concept of maintaining and updating …