Columnar Versus Row Storage

Concepts covered: paColumnarVsRow, paParquet, paVectorizedExecution

The single most important fact about a storage format is whether it lays out rows or columns contiguously on disk. The choice flips which queries are fast. A row store wins when queries fetch entire rows by key. A column store wins when queries scan a few columns across many rows. Modern analytical workloads are dominated by the second pattern, which is why every cloud warehouse and every serious lake format uses columnar storage. How the Bytes Are Arranged Consider a table with twenty columns and a billion rows. A query computes the sum of one column. A row store reads the bytes of all twenty columns for every row, throwing away nineteen twentieths of what was read. A column store reads only the bytes for that one column. The reduction in bytes scanned is roughly twenty to one. The reduct

About This Interactive Section

This section is part of the Storage Layers and Table Formats: Intermediate lesson on DataDriven, a free data engineering interview prep platform. Each section includes explanations, worked examples, and hands-on code challenges that execute in real time. SQL queries run against a live PostgreSQL database. Python runs in a sandboxed Docker container. Data modeling problems validate against interactive schema canvases. All content is framed around what data engineering interviewers actually test at companies like Meta, Google, Amazon, Netflix, Stripe, and Databricks.

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