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1. Column-oriented systems are more efficient when an aggregate needs to be computed over many rows but only for a notably smaller subset of all columns of data, because reading that smaller subset of data can be faster than reading all data.
2. Column-oriented systems are more efficient when new values of a column are supplied for all rows at once, because that column data can be written efficiently and replace old column data without touching any other columns for the rows.
3. Data compression - Column data is of uniform type; therefore, there are some opportunities for storage size optimizations available in column-oriented data that are not available in row oriented data.
Data types supported by the In-Memory Database
The Yellowfin in-memory database is designed to allow you to access the following data types for analysis and reporting.
* All standard numeric data types (eg. int, decimal, float, binary)
* All standard character data types (eg. char, nchar, varchar, nvarchar, text)
* All standard time data types (eg. timestamp, datetime)
* GIS data (GIS points, GIS polygons)
* BLOB & CLOB data