Refresh Strategies & Materialized Views

A summary table is only useful if it stays current. The refresh strategy determines how often the summary is updated, how long it takes, and what happens when it fails. This is the operationally consequential part of pre-aggregation. Full Refresh vs Incremental The production pattern: incremental refresh daily, full refresh weekly as a reconciliation check. If the full refresh produces different numbers than the incremental, you have a bug in the incremental logic. The weekly full refresh catches it. Materialized Views A materialized view is a database-managed summary table. You define the query, and the database handles storage and refresh. This is the simplest form of pre-aggregation because you do not need to build or schedule ETL jobs. The database does it for you. Platform Comparison

About This Interactive Section

This section is part of the Pre-Aggregation 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.

How DataDriven Lessons Work

DataDriven combines four interview rounds (SQL, Python, Data Modeling, Pipeline Architecture) with adaptive difficulty and spaced repetition. Easy problems get harder as you improve. Weak concepts resurface until you master them. Your readiness score tracks progress across every topic interviewers test. Every lesson section ends with problems you solve by writing and running real code, not by picking multiple-choice answers.