What is columnar storage?
Columnar storage is a way of storing table data column-by-column rather than row-by-row, so analytical queries can read only the columns they need and compress data efficiently. It makes aggregations and scans over large datasets much faster, which is why analytical databases and warehouses use it.
Row-oriented storage keeps all of a record's fields together, ideal for transactional apps that read and write whole rows. Columnar storage instead groups each column's values together, which suits analytics.
Advantages for analysis:
- Read less a query touching three columns skips the rest entirely.
- Compress more similar values stored together compress well.
- Vectorised aggregation summing a column is very fast.
This is why OLAP databases, data warehouses and lakehouse formats lean on columnar storage, while OLTP systems stay row-oriented for fast writes.
iDBQuery works efficiently with both. When it queries a columnar warehouse or analytical store, the SQL it generates benefits from that engine's column-oriented speed for big aggregations; when it queries a row-based operational database, it writes queries suited to that too. You do not have to think about the underlying storage, iDBQuery adapts the query it generates to each source. Whatever the storage model, you ask in plain language and get a fast, cited answer traced back to the source rows, across one source or many joined together.
Updated 2026-06-22