What is an advantage of using columnar storage in Databricks?

Using columnar storage in Databricks offers significant advantages, particularly in terms of data compression efficiency. In a columnar storage format, data for each column is stored together, leading to more effective compression techniques that can significantly reduce the size of the data on disk. This is because similar types of values are stored consecutively, allowing for better utilization of compression algorithms.

Since columnar storage groups data by column rather than by row, it is easier to identify and eliminate redundancies across large datasets, leading to higher compression ratios compared to row-oriented storage. This results in reduced storage costs and improved performance when reading and manipulating large datasets during analytical queries, as less data needs to be read from disk.

As a result, utilizing columnar storage not only enhances storage efficiency but also positively impacts query performance, especially for analytical workloads where aggregation and filtering on specific columns are common. This makes it a preferred choice for analytical queries in data lakes and data warehouses.