When working with Snowflake, you’ll eventually come across views and materialized views. Both can help organize data and simplify queries, but they work differently behind the scenes. Choosing between them depends on what you’re trying to achieve, how often the underlying data changes, and how important query performance is for your workload.
If you’re exploring Snowflake concepts through Snowflake Training in Chennai, understanding the difference between these two types of views is useful because views are commonly used in analytics, reporting, and data engineering projects.
So, what exactly is the difference between a Snowflake view and a materialized view? Let’s break it down in simple terms.
What Is a View in Snowflake?
A view is essentially a saved SQL query.
Instead of writing the same query repeatedly, you can create a view and query it like a table.
For example:
CREATE VIEW customer_orders AS
SELECT
customer_id,
order_id,
order_amount
FROM orders
WHERE order_status = ‘COMPLETED’;
You can then query the view:
SELECT *
FROM customer_orders;
The important thing to understand is that a standard view doesn’t store a separate copy of the query results. When you query the view, Snowflake evaluates the underlying query against the source data.
This makes views convenient when you want a reusable way to access or present data.
What Is a Materialized View?
A materialized view is different because Snowflake stores the results of the query in a materialized form.
For example:
CREATE MATERIALIZED VIEW sales_summary AS
SELECT
product_id,
SUM(order_amount) AS total_sales
FROM orders
GROUP BY product_id;
Instead of calculating the aggregation from scratch every time a query accesses the materialized view, Snowflake can use the stored results. Snowflake also maintains the materialized view as the underlying data changes. This can make materialized views useful for certain workloads where the same expensive query is executed frequently.
Snowflake Views vs. Materialized Views: Key Differences
The simplest way to understand the difference is:
View: Stores the query definition.
Materialized View: Stores the query results in a maintained form.
Imagine you run a complex sales calculation every time someone opens a dashboard. With a standard view, the underlying query needs to be evaluated when the view is queried. With a materialized view, Snowflake can use the maintained materialized results, potentially reducing the work required for repeated queries.
How Do Standard Views Work?
Let’s say you have a large orders table and frequently need completed orders.
You could create:
CREATE VIEW completed_orders AS
SELECT *
FROM orders
WHERE status = ‘COMPLETED’;
The view doesn’t create another full copy of the data.
When users query:
SELECT *
FROM completed_orders;
Snowflake works with the underlying table according to the view definition.
This approach is useful when you want a logical layer over existing data.
How Do Materialized Views Work?
Now imagine you have a large sales table and frequently calculate total sales by product.
Instead of repeatedly performing the same aggregation, you could create a materialized view.
CREATE MATERIALIZED VIEW product_sales AS
SELECT
product_id,
SUM(order_amount) AS total_sales
FROM orders
GROUP BY product_id;
Snowflake maintains the materialized results as the underlying data changes. When queries use the materialized view, Snowflake can use the precomputed information rather than performing the complete aggregation from the base table every time. This can be particularly useful for workloads where complex calculations are performed repeatedly.
When Should You Use a Standard View?
Standard views are a good choice when you mainly need a reusable query layer.
They can be useful when:
- You want to simplify complex SQL.
- You want to provide users with a consistent way to access data.
- You don’t need to store precomputed results.
- The underlying data changes frequently.
- You want to avoid creating another materialized data structure.
For example, suppose an organization wants analysts to access only specific columns from a customer table.
A view can provide that simplified interface without requiring a separate copy of the data.
When Should You Use a Materialized View?
Materialized views are more suitable for specific performance-oriented use cases.
They can be useful when:
- Queries perform expensive calculations repeatedly.
- The same aggregation is accessed frequently.
- Improving query performance is important.
- The workload benefits from precomputed results.
For example, a dashboard that repeatedly calculates large aggregations may benefit from a materialized view.
However, materialized views aren’t automatically the best choice for every query.
Performance Considerations
Performance is one of the biggest reasons organizations consider materialized views. Suppose an analytical query scans a large table and performs several calculations every time it runs.
If the same result is requested repeatedly, recalculating everything can require significant compute resources.
A materialized view can reduce the amount of work needed for certain queries by maintaining the results.
However, there is a trade-off.
Because Snowflake needs to maintain the materialized view when the underlying data changes, materialized views can introduce additional compute and storage considerations.
That’s why they should be used based on actual workload requirements rather than simply adding them everywhere.
Views and Materialized Views for Data Security
Views can also be useful for controlling what information users see.
For example, a company may have a customer table containing several sensitive fields but want analysts to access only the fields required for reporting.
A view can expose selected columns or filtered records.
This creates a controlled logical layer between users and the underlying table.
Materialized views can also have access-control considerations, but their primary purpose is generally related to performance and precomputed results rather than simply creating an access layer.
A Simple Example
Imagine a retail company with a huge sales table.
The analytics team frequently asks:
“What are the total sales for each product?”
A standard view could contain the aggregation query, but the calculation would still need to be evaluated when the view is queried.
A materialized view could maintain the aggregated results, making it a potential choice when this calculation is requested frequently and the workload justifies the additional maintenance.
The best option depends on factors such as query frequency, data volume, data-change patterns, and performance requirements.
Common Mistakes to Avoid
One common mistake is assuming that materialized views are always faster. They can improve performance for suitable workloads, but they also require maintenance as source data changes. Another mistake is creating materialized views without checking whether the query is actually a performance bottleneck.
It’s better to first understand your workload, identify expensive or frequently repeated queries, and then determine whether a materialized view makes sense. Similarly, standard views shouldn’t be avoided simply because they don’t store query results. They remain extremely useful for simplifying SQL and creating logical data-access layers.
Final Thoughts
The difference between Snowflake views and materialized views mainly comes down to how the query results are handled.
A standard view stores the SQL definition, while a materialized view maintains query results that Snowflake can use for suitable workloads.
If your goal is to simplify SQL, create reusable data-access logic, or provide users with a controlled view of underlying data, a standard view may be enough. If you’re dealing with frequently repeated and computationally expensive queries, a materialized view may be worth considering.
The key is to choose based on your actual workload rather than treating one option as universally better.
For learners building practical Snowflake and data engineering knowledge, Qmatrix Technologies focuses on helping learners understand concepts such as views, materialized views, SQL optimization, data pipelines, and Snowflake architecture through practical, industry-oriented learning.