SQL Views Every Data Analyst Should Master
SQL Views are often overlooked and not as popular, not heard by many. I myself got to know about the SQL views when I started my journey as a Data Analyst. While I was working on my SQL assignment. I found SQL views a very important and useful tool that a data analyst can utilize to increase its productivity.
If you've ever worked with databases, you've probably repeated the same SQL queries over and over. That's where SQL Views come in, they make managing queries easier and more efficient by which a Data Analyst can save time.
Why do a Data Analyst need SQL views?
SQL Views make a Data Analyst work easier, safer and faster. A Data Analyst can control how much information to share, you can show only the columns or rows a client or stakeholder should see while keeping sensitive data hidden.
They save time - Instead of writing the same long query again and again, you save it once as a view and reuse it.
They hide complexity - Even if the real tables have many joins, a view gives you a clean, simple table to work with.
They protect sensitive data - You can show only the columns people should see. The rest stays secure and hidden in the base tables. example like in banks, where you do not want to share the account number, password, PIN.
They keep your analysis consistent - It means everyone gets the same results because everyone is using the same saved logic inside the view.
They make dashboards and reports easier to build - Tools like Tableau or Power BI can connect to a view just like a table, which keeps your data model clean.
We’ll walk through the different Types of SQL Views, understand when to use each one, and learn how to create a view, query a view, and drop a view. By the end, you’ll see how SQL Views can make your daily work as a data analyst more efficient.
I will be using Shop Dataset to explain the SQL Views. It will be like a real world example of how retail shops work and we get a understanding of how to use SQL views. In my Shop database I have the following Tables: customers, products, categories, orders , order items.
What is a SQL Views?
A SQL view is a virtual table based on the result set of an SQL SELECT statement. It doesn’t store data itself. Instead, it shows data that is actually in the base tables. Whenever you run a query on the view, it pulls the latest data from those tables and displays it like a normal table.
Create a SQL View-
Syntax-
CREATE VIEW view_name AS
SELECT column1, column2, ...
FROM table_name
WHERE conditions;
Once created, you can query the view as a regular table using SELECT
Syntax-
SELECT column1, column2, ...
FROM view_name
WHERE condition;
Update the SQL Views -
Updating a view in SQL means modifying the data it shows. There are two main ways .
1. You can update data through a view only if the view is “updatable.” When you update the view, the changes go straight to the base table.
Syntax of UPDATE VIEW -
UPDATE view_name
SET column1 = value1, column2 = value2, ...
[WHERE condition];
2. If you want to change "what a view shows", like adding or removing columns or updating the logic, you don’t update the data in base table. Else You update the view’s query using CREATE OR REPLACE VIEW (or ALTER VIEW in some databases).
Syntax using CREATE OR REPLACE VIEW -
CREATE OR REPLACE VIEW view_name AS
SELECT column1, column2, ...
FROM table_name
WHERE [condition];
Syntax using ALTER VIEW -
ALTER VIEW view_name AS
SELECT column1, column2, ...
FROM table_name
WHERE condition;
Drop a View -
Use the DROP VIEW statement to delete a view
Syntax
DROP VIEW view_name;
Types of SQL Views -
1. Regular View or Permanent View
A regular view is a saved query that always shows the latest data from your tables. It doesn’t store data — it just runs the SELECT every time you use it.
You can use it
when you simplify long queries
to hide complex joins
To give analysts a clean table to work with
Lets understand with our shop dataset , we will create a view that shows basic product information like product name, category, and price — without needing to join tables every time. For this we will combine products and categories tables and will show each product with its category name. This will save you from writing the big JOIN queries again and again. And This view always shows the latest data because it’s a regular view.
To get the Product list we will use SELECT
Dropping a Regular View
A regular view is removed using a simple DROP VIEW command. This deletes the view but does not affect the base tables.
2. Temporary View (Session‑Only View)
A temporary view exists only while your database session is open. Once you disconnect, it disappears. Its a short‑term view you create just for quick, one‑time analysis, and it automatically disappears when you close your database session.
You can use it
Quick analysis
Testing logic
Avoid cluttering your database
If we want to check for today ’orders we can use Temp Views. It's useful as you can just see today's data without using extra space or creating permanent objects.
To see the what orders we had today we can see by query the above temp view just like the regular table. But it disappears when your session ends.
Dropping a Temporary View
A temporary view is dropped the same way — but remember, it also disappears automatically when your session ends. If you forget to drop it, PostgreSQL will clean it up when you disconnect.
3. Materialized View (Stored View)
A materialized view actually stores the data. It’s like taking a snapshot of the query result.
You can use it
Heavy queries
Aggregations
Dashboards that don’t need real‑time data
Image credit - By Author Materialized views don’t update automatically. You have to refresh it when needed using REFRESH . Like if we want to see today's revenue of the shop. This is useful in a real-time scenario where we can check the daily revenue that the shop made on regular bases.
After Refreshing the view like shown above, to see the today revenue, you can see the revenue by SELECT query. Like this we can check daily.
Dropping a Materialized View
Materialized views use a slightly different command because they store data physically. This removes the stored snapshot and frees up space.
Best Practices -
When working with SQL views, it really helps to use clear and meaningful names so anyone reading your code immediately understands the purpose of the view.
Try to avoid complex nested views, because it can make debugging and performance difficult.
Make it a habit to document the logic behind each view, especially if it contains business rules or calculations that others will rely on.
For heavy analytical queries or large aggregations, it’s usually better to use materialized views, since they store the results and can significantly improve performance.
And above all, keep each view focused on a single purpose, so it stays clean, predictable, and easy to maintain.
When Not to Use Views -
Views are useful, but they’re not the best choice for every situation. It’s better not to use a view when you’re doing complicated calculation
when you need full control over how the query runs for performance reasons. In these cases, a view can make things slower or harder to optimize.
Conclusion -
SQL Views are one of the simplest ways to make your SQL cleaner, more secure, and easier to reuse across teams. They help you hide complexity, protect sensitive data, and build consistent logic that supports dashboards, analytics, and applications.
With a small shop database like the one we used here, we got understanding that how much more organized and efficient your analytics workflow becomes once you start using views as per your need.
For data analysts, SQL views can reduce repetitive work and make everyday analysis faster and more reliable.
