When building an analytics system, you often need to make complex data easier and faster to query. One common approach is to create a database view instead of repeatedly writing the same SQL query.

But there are two important types of views to understand: regular views and materialized views.

They may look similar from a data analyst’s perspective, but they behave very differently behind the scenes.

A regular view stores a SQL query definition and runs that query when you access the view. A materialized view, on the other hand, stores the result of the query so that future queries can read the precomputed data.

This difference can have a major impact on dashboard performance, reporting workloads, data freshness, storage, and database costs.

In this guide, we’ll compare materialized views and regular views, explain how each works, and show when analytics teams should use one over the other.

What Is a Regular View?

A regular view is a saved SQL query that behaves like a virtual table.

For example, suppose you have an orders table:

CREATE VIEW monthly_sales AS
SELECT
    DATE_TRUNC('month', order_date) AS month,
    SUM(order_amount) AS total_sales
FROM orders
GROUP BY DATE_TRUNC('month', order_date);

You can then query it like a table:

SELECT *
FROM monthly_sales;

The important thing to understand is that the database generally does not store the result of the query.

Instead, when you query the view, the database executes the underlying query.

Conceptually:

Analyst
   |
   v
monthly_sales view
   |
   v
Underlying SQL query
   |
   v
orders table

The view is therefore a reusable layer over the underlying data.

What Is a Materialized View?

A materialized view also contains a query definition, but the database stores the query’s result.

For example:

CREATE MATERIALIZED VIEW monthly_sales AS
SELECT
    DATE_TRUNC('month', order_date) AS month,
    SUM(order_amount) AS total_sales
FROM orders
GROUP BY DATE_TRUNC('month', order_date);

When the materialized view is created, the database calculates the result and stores it.

Later, when an analyst runs:

SELECT *
FROM monthly_sales;

the database can read the already-computed results instead of calculating the aggregation from the original orders table every time.

The basic architecture looks like this:

Analyst
   |
   v
Materialized View
   |
   v
Stored Query Results

This can significantly reduce the amount of work required for expensive analytical queries.

Regular Views vs Materialized Views

The biggest difference is simple:

A regular view stores the query. A materialized view stores the query result.

Feature	Regular View	Materialized View
Stores query definition	Yes	Yes
Stores query results	Usually no	Yes
Query execution	Usually happens when queried	Results are already computed
Storage required	Very little	Additional storage
Data freshness	Usually reflects current underlying data	Depends on refresh
Repeated expensive queries	Can be expensive	Usually much faster
Refresh required	No	Yes
Good for reusable SQL logic	Yes	Yes
Good for heavy aggregations	Sometimes	Often
Maintenance	Low	Higher
Indexing options	Database-dependent	Often possible depending on database
Best use case	Abstraction and reusable logic	Performance optimization

How Regular Views Work in Analytics

Imagine your company has a table containing millions of transactions.

You frequently need to calculate revenue by product category.

Without a view, analysts might repeatedly write:

SELECT
    category,
    SUM(revenue) AS total_revenue
FROM sales
GROUP BY category;

Instead, you could create:

CREATE VIEW category_revenue AS
SELECT
    category,
    SUM(revenue) AS total_revenue
FROM sales
GROUP BY category;

Now analysts can simply run:

SELECT *
FROM category_revenue;

This improves consistency because everyone uses the same business logic.

However, the underlying aggregation may still need to be performed whenever the view is queried.

If sales contains hundreds of millions of rows, repeatedly calculating the aggregation can become expensive.

How Materialized Views Work in Analytics

A materialized view can precompute the same result.

For example:

CREATE MATERIALIZED VIEW category_revenue AS
SELECT
    category,
    SUM(revenue) AS total_revenue
FROM sales
GROUP BY category;

The database calculates the result and stores it.

A dashboard query can then read:

SELECT *
FROM category_revenue;

Instead of repeatedly processing the entire sales table.

This is particularly useful when:

• The underlying dataset is large
• The query contains expensive joins
• Aggregations are computationally expensive
• Many users run the same query
• Dashboards repeatedly request the same metrics

The Data Freshness Difference

One of the most important differences between the two approaches is data freshness.

A regular view normally reflects changes in the underlying tables when it is queried.

Suppose a new order is inserted:

orders table
     |
     +-- New order
     |
     v
regular view
     |
     v
New result available

The view doesn’t generally need to be refreshed manually.

Materialized views are different.

If the materialized view was generated at 9:00 AM and new transactions arrive at 10:00 AM, the stored result may not include those transactions until the materialized view is refreshed.

For example:

REFRESH MATERIALIZED VIEW monthly_sales;

The exact syntax depends on the database system.

This creates an important analytics tradeoff:

Performance vs freshness.

Materialized View Refresh Strategies

Materialized views aren’t necessarily refreshed only once.

Analytics systems can use different refresh strategies depending on business requirements.

1. Manual Refresh

An engineer or administrator refreshes the materialized view when needed.

REFRESH MATERIALIZED VIEW sales_summary;

This can work for relatively static reporting datasets.

For example, a monthly financial report might only need to be refreshed once per day.

2. Scheduled Refresh

The database or orchestration system can refresh the view periodically.

For example:

12:00 AM
   ↓
Refresh materialized view
   ↓
6:00 AM
   ↓
Refresh again
   ↓
12:00 PM
   ↓
Refresh again

This is useful when dashboards don’t need real-time information.

A business dashboard might only need data that is 15 minutes, 1 hour, or 1 day old.

3. Incremental Refresh

Some database platforms can update only the portion of a materialized view affected by new or changed data.

Conceptually:

Existing materialized view
        +
New data
        ↓
Incremental update
        ↓
Updated materialized view

This can be significantly more efficient than rebuilding a large result from scratch.

However, support for incremental refresh varies between database technologies.

Why Materialized Views Can Make Dashboards Faster

Consider a Power BI dashboard that repeatedly asks for:

Revenue by:
- Month
- Region
- Product
- Customer segment

Suppose the underlying transaction table contains 500 million rows.

If every dashboard interaction causes the database to scan and aggregate those rows, performance can suffer.

A materialized view could pre-aggregate the information:

500 million transaction rows
          ↓
   Materialized View
          ↓
     50,000 rows

The BI tool can query the much smaller result.

This can reduce:

• Query execution time
• CPU usage
• Database workload
• Repeated computation
• Dashboard latency

Regular Views Are Not Automatically Slow

It’s important not to assume that regular views are always inefficient.

Modern databases can optimize view queries.

For example, a database optimizer may:

• Push filters into underlying queries
• Eliminate unnecessary operations
• Optimize joins
• Choose efficient indexes
• Rewrite parts of the query plan

Consider:

SELECT *
FROM customer_sales
WHERE region = 'West';

If customer_sales is a regular view, the database may optimize the underlying query rather than blindly calculating everything first.

So the decision shouldn’t simply be:

“Materialized views are faster.”

A better question is:

“Is the repeated computation expensive enough to justify storing the result?”

When Should You Use a Regular View?

Regular views are often appropriate when simplicity, consistency, and freshness are more important than precomputed performance.

Use regular views when:

1. The query is relatively inexpensive

If the underlying query executes quickly, materializing it may add unnecessary complexity.

2. Data must be highly current

If analysts need the latest records immediately, a regular view can be useful because it generally queries the underlying tables directly.

3. You want to hide complex SQL

A view can provide a clean interface for analysts.

Instead of exposing:

orders
customers
products
payments
refunds
shipping

you can provide:

analytics_customer_sales

with the required joins already defined.

4. You want centralized business logic

For example:

CREATE VIEW active_customers AS
SELECT *
FROM customers
WHERE status = 'active';

Everyone can now use the same definition of an active customer.

When Should You Use a Materialized View?

Materialized views become attractive when query performance is a significant problem.

Use materialized views when:

1. Queries are expensive

Complex joins and aggregations can benefit from precomputed results.

2. The same query runs frequently

If hundreds of dashboard users repeatedly request the same aggregation, materializing it can reduce duplicated computation.

3. Data does not need to be real-time

If a dashboard can tolerate data being 15 minutes, 1 hour, or 1 day old, materialization becomes more practical.

4. The source data is very large

Materializing an aggregation can dramatically reduce the number of rows that downstream analytics systems need to process.

5. BI dashboards are experiencing performance problems

Materialized views can act as a performance layer between large operational or analytical tables and BI tools.

A Practical Analytics Example

Suppose an e-commerce company has these tables:

customers
orders
products
payments

The analytics team wants a dashboard showing:

Revenue
Orders
Average Order Value
Revenue by Category
Revenue by Region

A regular view might combine all the relevant data:

CREATE VIEW analytics_sales AS
SELECT
    o.order_id,
    o.order_date,
    c.region,
    p.category,
    o.order_amount
FROM orders o
JOIN customers c
    ON o.customer_id = c.customer_id
JOIN products p
    ON o.product_id = p.product_id;

This creates a clean analytical interface.

But imagine the dashboard repeatedly aggregates millions of rows.

The team could create a materialized view:

CREATE MATERIALIZED VIEW daily_sales_summary AS
SELECT
    DATE_TRUNC('day', order_date) AS sales_date,
    region,
    category,
    COUNT(*) AS orders,
    SUM(order_amount) AS revenue,
    AVG(order_amount) AS average_order_value
FROM analytics_sales
GROUP BY
    DATE_TRUNC('day', order_date),
    region,
    category;

Now the BI dashboard can query:

SELECT *
FROM daily_sales_summary;

Instead of repeatedly processing the entire transaction dataset.

Materialized Views vs Tables

A common question is:

If a materialized view stores data, why not just create a table?

There is an important distinction.

A normal table is generally populated and maintained through explicit data-loading processes.

A materialized view is associated with a query that defines how its data is produced.

Conceptually:

Table
  ↓
Explicit data pipeline
  ↓
Stored data

Whereas:

Source tables
  ↓
Materialized view query
  ↓
Stored query result

The exact implementation and refresh capabilities depend on the database.

For analytics engineering, materialized views can therefore be useful as a middle ground between repeatedly calculating a query and maintaining a completely separate derived table.

Materialized Views and Data Warehouses

Materialized views are especially relevant in analytical databases and cloud data platforms.

Consider a typical architecture:

Operational Systems
       ↓
    Ingestion
       ↓
   Data Warehouse
       ↓
Materialized Views
       ↓
   BI / Analytics

The materialized view can act as a performance optimization layer.

For example:

Raw Events
   ↓
Millions of rows
   ↓
Daily aggregation
   ↓
Materialized View
   ↓
Thousands of rows
   ↓
BI Dashboard

This approach can be useful when many downstream queries need the same precomputed metrics.

Regular Views and Data Governance

Regular views aren’t only about convenience.

They can also help control how analysts access data.

For example, instead of giving every analyst direct access to a sensitive table, an organization could expose a view containing only approved fields.

CREATE VIEW customer_analytics AS
SELECT
    customer_id,
    country,
    customer_segment,
    signup_date
FROM customers;

Sensitive fields can be excluded from the analytical interface.

This can make views useful as part of a broader data governance strategy.

However, a view by itself shouldn’t be treated as a complete security solution. Access controls and database permissions still matter.

Performance Isn’t the Only Consideration

When deciding between a regular and materialized view, performance is only one factor.

You should consider at least five things:

Question	Regular View	Materialized View
Do users need the latest data?	Strong fit	Depends on refresh
Is the query expensive?	May be inefficient	Strong fit
Does the query run frequently?	Possible repeated cost	Strong fit
Is additional storage acceptable?	Minimal	Required
Can the system manage refreshes?	No refresh needed	Required

This makes the choice more of an architectural decision than simply a SQL preference.

A Simple Decision Framework

You can use this rule when designing an analytics system:

Is the query expensive?
        |
       No
        ↓
Use a regular view
        |
       Yes
        ↓
Does the data need to be real-time?
        |
       Yes
        ↓
Consider a regular view
plus other performance optimizations
        |
       No
        ↓
Does the query run frequently?
        |
       Yes
        ↓
Consider a materialized view

Of course, database-specific optimization features can change the answer.

Don’t Use Materialized Views as a First Resort

A materialized view can improve performance, but it also introduces another object that needs to be managed.

You now have to think about:

• Refresh frequency
• Refresh failures
• Storage
• Dependency management
• Data freshness
• Query performance
• Monitoring
• Permissions
• Schema changes

Before creating one, first investigate why the query is slow.

Possible alternatives include:

• Better indexes
• Query optimization
• Partitioning
• Clustering
• Better join strategies
• Pre-aggregation
• Warehouse optimization
• Better data modeling

A materialized view should solve a real performance or workload problem rather than being added simply because it sounds faster.

How Materialized Views Fit Into Modern Analytics

Modern analytics architectures often contain several layers:

Source Systems
      ↓
Raw Data
      ↓
Cleaned / Transformed Data
      ↓
Analytical Models
      ↓
Materialized Aggregations
      ↓
BI Tools

Regular views can provide reusable semantic or logical layers.

Materialized views can provide precomputed performance layers.

Both can therefore coexist in the same architecture.

For example:

Raw tables
   ↓
Regular view
   ↓
Business logic
   ↓
Materialized view
   ↓
Dashboard

This isn’t necessarily an either-or decision.

A mature analytics environment may use both.

Key Takeaways

The difference between regular and materialized views becomes much easier to understand when you focus on where the computation happens.

A regular view generally stores the SQL definition and calculates its result when queried.

A materialized view stores the result of the query and updates that result through a refresh process.

Use regular views when you primarily need:

• Reusable SQL logic
• Data abstraction
• Centralized business definitions
• Fresh underlying data
• Low maintenance

Consider materialized views when you need:

• Faster repeated queries
• Precomputed aggregations
• Better dashboard performance
• Reduced processing of large datasets
• A controlled freshness window

The most important question isn’t “Which one is better?”

It’s:

Does your workload benefit more from calculating the result on demand or storing a precomputed result that can be refreshed?

For small and moderately complex analytical queries, regular views may be all you need. For expensive, repetitive workloads over large datasets, materialized views can become an important performance optimization.

Frequently Asked Questions