Predicate Pushdown

Concepts covered: paColumnarVsRow

What They Want to Hear 'Predicate pushdown pushes the WHERE clause to the storage layer. In Parquet, each row group stores min/max statistics per column. If the query asks for revenue > 1000 and a row group's max revenue is 500, the entire row group is skipped without reading it. Combined with partition pruning, this can skip 99%+ of the data.' The key insight they are testing: pushdown works at TWO levels: partition pruning (skip folders) and row group pruning (skip chunks within files).

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This section is part of the Where Data Lives: 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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