When is it preferable to use a column store table over a row store table?

Using a column store table is particularly advantageous when performing frequent aggregations and analyses due to how data is organized and accessed in these types of tables. In a column store, data is saved by column rather than by row, which allows for more efficient reading of only the necessary columns when executing analytical queries. This results in faster query performance, especially in scenarios that require operations like SUM, AVG, or other aggregate functions across a large dataset.

Column stores are optimized for these operations because they can read and process only the relevant data from selected columns instead of scanning entire rows. This leads to reduced I/O usage and, consequently, improved response times in analytical workloads. In environments where aggregations and complex analyses on data are commonplace, utilizing a column store table will yield significant performance benefits over a row store table.

In contrast, full-text searches require quick access to entire rows of data to evaluate text against search terms, making row stores more suitable. When data consists of distinct values, such as categorical data, this doesn't necessarily indicate the preference for column stores as row stores can handle this effectively. Finally, when working with a small dataset, the performance differences between column and row stores may not be significant, so other factors such as query complexity or access patterns typically determine