Partitioning and File Pruning

Concepts covered: paPartitioning, paClusteringKey, paFilePruning

Columnar layout helps a query read fewer columns. Partitioning helps a query read fewer files. The combination is what turns an analytical scan from minutes into seconds. Partitioning splits a table into separate file paths organized by the value of one or more partition columns. A query with a filter on a partition column reads only the matching paths. Done well, partitioning is the largest single performance improvement available to a data engineer. Done badly, it produces millions of small files that destroy performance worse than no partitioning at all. Hive-Style Partition Layout The folder names encode partition values. A query engine like Spark, Trino, Athena, or Snowflake recognizes the convention and uses it to prune files. A query filtering on event_date = '2026-04-22' reads two

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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