Column-oriented databases are optimized for analytical workloads by storing data column-wise rather than row-wise. In a column-oriented structure, all the values for a particular column are stored together, enabling highly efficient aggregation and filtering operations. This design is particularly advantageous for read-heavy operations and OLAP (Online Analytical Processing) systems. For instance, Apache Cassandra, introduced in 2008, adopts a columnar approach to provide database scalability and database performance in distributed environments.
https://en.wikipedia.org/wiki/Column-oriented_DBMS
Unlike traditional row-oriented databases, which store complete rows together, column-oriented databases are better suited for scenarios where queries frequently aggregate data across columns. For example, a query summing sales figures over time benefits from retrieving a single column instead of scanning entire rows. This efficiency makes them ideal for use cases such as data warehouses, business intelligence tools, and big data analytics platforms like Apache HBase (introduced in 2008) and Google Bigtable (introduced in 2005).
https://en.wikipedia.org/wiki/Column_store
To achieve high performance, column-oriented databases often employ advanced database compression techniques, as columns tend to have repetitive data that compresses well. Furthermore, this database storage format minimizes I/O overhead by reducing the amount of irrelevant data read during SQL queries. Examples like Amazon Redshift (introduced in 2012) and Snowflake showcase the practical application of column-oriented design in modern cloud-based analytics solutions.