Columnar Storage & Nesting

Concepts covered: dmColumnarStorage

How Nested Data Interacts with Columnar Engines Modern analytical databases (BigQuery, Snowflake, Databricks) use columnar storage: data is stored column-by-column, not row-by-row. A query that reads only 3 columns out of 50 only scans those 3 columns. This is why analytical queries are fast. But nested data adds complexity. STRUCT sub-fields are stored as individual columns in columnar format. address.city is stored alongside address.state and address.zip as separate column chunks. Accessing one sub-field does not read the others. This is efficient. ARRAY elements are stored using repetition and definition levels (Parquet's Dremel encoding). This is more complex than flat columns. Deeply nested arrays (arrays of structs of arrays) create encoding overhead that degrades scan performance. P

About This Interactive Section

This section is part of the Nested Data 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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