View vs Table in SQL: Differences and When to Use Each

Views are virtual tables based on queries. They don't store data themselves but provide a simplified or secured window into your actual tables.

What Is the Difference Between a View and a Table?

A table is the physical storage layer: rows live on disk, and INSERT, UPDATE, and DELETE act on them directly. A view is a named query definition stored in the database catalog — querying it re-executes that query against the base tables. The view itself holds zero rows, so it never goes stale and takes almost no storage.

	View	Table
Storage	Virtual — no data stored (only the query definition)	Physical data storage on disk
Freshness	Always shows current data	Stores whatever was written (can hold historical snapshots)
Speed	As fast as the query it wraps	Direct reads; can be indexed
Writes	Limited (only simple single-table views are updatable)	Full INSERT / UPDATE / DELETE
Main purpose	Simplify complex queries, security layer (hide columns)	Store and manipulate the actual data

For a deeper side-by-side (updatability rules, indexing, permissions), see SQL view vs table.

Do Views Store Data?

No. A regular view stores only its SELECT statement — the data always lives in the underlying tables, and the database runs the view's query fresh on every access. That's why views never need refreshing and cost almost no storage. The one exception is a materialized view, which physically saves the query result and must be refreshed to pick up changes.

Are Views Faster Than Tables?

No — a regular view has no performance advantage, because it is just a saved query. Querying a view costs exactly the same as running its underlying SELECT, joins and all. Views exist for simplicity and security, not speed. If you need speed, use a materialized view or a summary table, which trade freshness for precomputed results.

Creating a Simple View

CREATE VIEW active_customers AS
SELECT customer_id, name, email, registration_date
FROM customers
WHERE status = 'active' AND deleted_at IS NULL;

Using Views Like Tables

-- Query the view just like a table
SELECT * FROM active_customers
WHERE registration_date >= '2024-01-01';

Complex View with Joins

Views shine at packaging multi-table logic — like this LEFT JOIN aggregation — into a single reusable name:

CREATE VIEW customer_order_summary AS
SELECT
    c.customer_id,
    c.name,
    c.email,
    COUNT(o.order_id) as total_orders,
    SUM(o.total) as lifetime_value,
    MAX(o.order_date) as last_order_date
FROM customers c
LEFT JOIN orders o ON c.customer_id = o.customer_id
GROUP BY c.customer_id, c.name, c.email;

Materialized Views: When a View Does Store Data

A materialized view runs its query once and physically stores the result, so reads are as fast as reading a table. The trade-off is freshness: the data is only as current as the last refresh. Use them for expensive aggregations that many queries share — dashboards, daily rollups, reporting layers.

-- PostgreSQL / Oracle
CREATE MATERIALIZED VIEW daily_revenue AS
SELECT order_date, SUM(total) AS revenue
FROM orders
GROUP BY order_date;

-- Re-run the query and replace the stored result
REFRESH MATERIALIZED VIEW daily_revenue;

SQL Server's equivalent is an indexed view (kept up to date automatically); MySQL has no native materialized views, so people simulate them with summary tables refreshed by a scheduled job. BigQuery and Snowflake maintain theirs incrementally and automatically.

When to Use Views

• Hide sensitive columns (e.g., salaries, passwords)
• Simplify complex joins for end users — see SQL INNER JOIN explained
• Create consistent calculated fields
• Maintain backward compatibility when table structure changes