Which of these is NOT a feature of column store tables?

Column store tables in a database are specifically designed to enhance the performance of analytical queries, which often involve reading vast amounts of data for aggregation and reporting purposes. One of the standout features of column store tables is their ability to efficiently compress data. By organizing data into columns rather than rows, these tables can leverage redundancy and repeated values effectively, leading to significant savings in storage space.

Faster read access is another key benefit of column store tables, particularly for analytical queries. This structure allows the database management system to read only the relevant columns required for a query instead of the entire row, greatly speeding up data retrieval.

While optimized for aggregations, column store tables excel at handling operations like SUM, COUNT, AVG, and other calculations that are critical in reporting and analytical scenarios. This optimization stems from the way data is stored, allowing quick access to aggregable data.

In contrast, column store tables are not typically optimized for transaction processing, which often requires frequent updates and rapid row-level changes. This characteristic makes them less suitable for scenarios that involve a high volume of writes or updates, which are common in transactional databases. Therefore, while column store tables shine in read-heavy analytical workloads, they are not ideal for environments focused on transaction processing that require faster updates.