Column Store Databases are awesome! - by Dilovan Celik

In recent years, I’ve seen a growing trend of clients moving their data platforms to solutions like Snowflake, Databricks, or DuckDB. These platforms have become wildly popular for analytics workloads, largely because of one standout feature: column-level data storage. To understand why this is such a game-changer, it helps to start with what it’s replacing: the traditional row-level storage.

Most applications are built to handle transactional workloads rather than analytical ones. They treat data as individual records to be stored, retrieved, and modified—think of a database as a filing cabinet, where each record contains all the details for a specific entity.

In a traditional row-level storage model, each record is stored as a row, similar to how a CSV file organizes data. Here’s an example of what this looks like:

When data is stored this way, all the information for a single row is physically grouped together on the disk. This makes it extremely efficient to retrieve entire rows of data—perfect for CRUD (Create, Read, Update, Delete) operations.

Leave a Comment