Materialized View
A precomputed query result stored as a physical table and refreshed periodically. Trades storage for read performance on expensive aggregations.
What is Materialized View?
A precomputed query result stored as a physical table and refreshed periodically. Trades storage for read performance on expensive aggregations.
Materialized View is a intermediate-level concept that sits in the Database Types & Storage area of system design. Engineers reach for it whenever they need to reason about real-world trade-offs in that space — not just for textbook correctness, but because real production systems at companies like Netflix, Amazon, and Google make these decisions every day.
If you want to go deeper than this definition — with diagrams, code, and a quiz to lock it in — work through the "Materialized View" lesson linked below. It walks through the why, the mechanism, the trade-offs, and how the giants actually use it in production.
Learn Materialized View in depth
Full interactive lesson with diagrams, code examples, real-world references, and a quiz.
Open the Materialized View lessonRelated lessons
Lessons that touch on Materialized View as part of a larger topic.
Materialized Views
Pre-computed query results stored on disk, the bridge between views and denormalization
foundation · database fundamentals
Data Aggregation
Combine data from multiple sources into a unified view, building the single source of truth
intermediate · data governance compliance
Stream-to-Table Joins
Enriching stream events with reference data, lookup joins, broadcast state, and materialized views
advanced · stream batch processing
See also
Related glossary terms you might want to look up next.
Database View
A virtual table defined by a SQL query. Simplifies complex joins, enforces access control, and presents data in a specific shape without duplicating storage.
Caching
Storing frequently accessed data in a faster storage layer so you don't have to fetch it from the original (slower) source every time.
Denormalization
Intentionally adding redundant data to database tables to speed up read queries by avoiding expensive joins. Trades storage and write complexity for read performance.