Types of Views in SQL: Explore Key Variants and Their Advantages and Limitations

Did You Know? In SQL, materialized views are so powerful that they can store the result of a query physically, unlike regular views that just store the query itself. This makes them ideal for real-time analytics on massive datasets, and they're heavily used in data warehousing systems like Oracle, PostgreSQL, and BigQuery

SQL views act like customized windows into your database, allowing you to present only the data you need without altering the original tables. For instance, if your sales team needs access only to customer names and purchase totals (but not sensitive financial details), you can create a view that filters and displays exactly that. This makes data easier to query, reduces complexity, and enhances security.

In this blog, we’ll explore the different types of views in SQL, such as simple, complex, materialized, and inline views. You’ll learn how to create, modify, and update them effectively—plus best practices for performance optimization and access control across multi-user environments.

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Types of Views in SQL: An Overview

Different types of views exist to serve varying use cases. For instance, a simple view might present selected columns from a single table, useful for hiding sensitive data or simplifying user queries. In contrast, complex views may involve joins, aggregations, or subqueries, helping developers encapsulate intricate logic and present it in an accessible format.

The reason for having different types of views is primarily to support data abstraction, performance optimization, and security. Materialized views, for example, store the result of a query physically and are used when the underlying data is expensive to compute repeatedly, improving query performance in data-heavy systems. 

Inline views, often used as subqueries in the FROM clause, help break down complex queries for better readability and modular design. By offering these varied types, SQL enables developers and analysts to tailor data presentation and access in ways that align with the needs of applications, user roles, and system performance constraints.

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Understanding Views in SQL

A view is a carefully defined query that presents data as though it were a table. It does not store rows on disk, so it always stays connected to underlying tables. Each time you query the view, it retrieves current information according to the conditions or columns you set. 

You can highlight only the details you want, which can simplify work on large tables and keep sensitive columns out of reach. This design is especially helpful when you want consistent and secure data access without complicating your base tables.

Below are the key characteristics that define what makes a view distinct in a database:

• No Physical Storage: A standard view retrieves data on demand. It does not hold its own permanent copy of the underlying records.
• Controlled Exposure: You can reveal only the columns or rows you want, which keeps sensitive information out of sight.
• Reusable Queries: A view encapsulates filtering or joining logic in one object, reducing duplicated SQL code.
• Adaptable to Schema Changes: A table can change columns or structure, yet a well-defined view can still present a consistent interface.

Let’s understand this through an example.

Instead of repeatedly writing a complex query to fetch sales data for the current year, you can create a view that does the work for you:

CREATE VIEW CurrentYearSales AS
SELECT OrderID, CustomerID, TotalAmount
FROM Sales
WHERE YEAR(OrderDate) = YEAR(GETDATE());

This view, CurrentYearSales, allows you to fetch the data directly with a simple SELECT * FROM CurrentYearSales, eliminating repetitive query writing.

Sample Output:

Assuming today is May 27, 2025, and your Sales table has the following data:

Then, the output of:

SELECT * FROM CurrentYearSales;

would be:

Only rows with OrderDate in 2025 (the current year) are returned.

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Also Read: SQL for Data Science: Functions, Queries, and Best Practices

How Do Different Types of Views in DBMS Work Internally?

A view behaves like a virtual table, which runs a stored SELECT query each time it is accessed. A physical table, in contrast, stores its records and updates them directly whenever data is inserted or deleted. The contents of a virtual table never occupy permanent space, and they only refresh when someone queries the view.

Here is a brief outline of how they operate:

• Instant Reflection: A query on a view pulls the latest records from its underlying data source.
• Defined by SELECT: The logic in the defining SELECT statement decides which rows and columns appear.
• No Direct Record Management: You adjust real data in the base table; the view only reflects those changes.
• Indexing: An indexed or materialized view can store results for quicker queries, but it refreshes according to the settings you choose.

Also Read: The Essential Guide To Understanding SQL In DBMS

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What Are the Different Types of Views in SQL?

Some types of views in SQL rely on calculations that run each time, while others hold separate copies behind the scenes. They all share one goal: keep your queries concise and your project organized. 

Choosing the right type can reduce overhead when filtering or combining data. Below is a closer look at the main types of views in DBMS you might use, along with their strong points.

1. System-Defined Views

System-defined views come prepackaged in the database engine. They store details about your database structure and system-wide metrics, which helps you check on tables, columns, or even server statistics without manual tracking. They can also show which objects depend on each other, so you can plan changes more accurately.

Below is a quick example of how such a view might be used.

You can run the snippet below to see a list of all databases in your SQL environment, along with their IDs and creation dates:

SELECT name, database_id, create_date
FROM sys.databases;

This query displays the internal metadata that the server keeps about your databases. The result set tells you the database names, numerical IDs, and the day each was created. Although these results stem from a built-in view, you can still filter or join them with other data as needed.

System-defined views are further broken down into three types. Let’s explore each one in detail.

Information Schema Views

Information schema views are designed to show details about objects in your database. They often begin with INFORMATION_SCHEMA and can reveal everything from columns and constraints to table relationships.

Characteristics Of Information Schema Views

• Schema-Level Focus: They concentrate on tables, columns, and constraints at the schema level.
• Unified Standards: They follow an SQL standard that many platforms support similarly.
• Consistent Naming: The naming pattern typically starts with INFORMATION_SCHEMA, followed by a descriptor like TABLES or COLUMNS.

Example Of Information Schema View
Below is a snippet that returns all the columns in a specified table:

SELECT COLUMN_NAME, DATA_TYPE
FROM INFORMATION_SCHEMA.COLUMNS
WHERE TABLE_NAME = 'YourTableName';

This query returns every column’s name and data type in the chosen table. You get a quick overview of column characteristics without browsing through multiple system tables. This helps when you need a standardized way to see the schema layout.

Catalog Views

Catalog views hold metadata about various database objects, such as indexes or triggers. They help you find information that might not be directly exposed in an information schema view.

Characteristics Of Catalog Views

• Wide-Ranging Metadata: They can show objects, indexes, triggers, and more.
• Tied To Internal Tables: They read from system base tables but present the results in a more organized structure.
• Detailed Insights: They often reveal information such as object creation dates and internal IDs.

Example Of Catalog View
You can see a list of all user-defined tables in the current database by running this:

SELECT name, object_id
FROM sys.tables;

The results reveal each table’s name and a unique identifier. You can pair this identifier with other catalog views or system-defined tables to learn more, such as dependencies or indexing details.

Dynamic Management Views (DMVs)

Dynamic Management Views show the present state of the server, databases, or running sessions. They are valuable when assessing performance or checking for blocking queries.

Characteristics Of Dynamic Management Views

• Real-Time Insights: They give you immediate performance or health data.
• Two Levels: Some DMVs show server-wide stats, while others focus on a single database.
• Diagnostic Aid: They help you identify slow queries, memory usage, or locking problems.

Example Of DMV
Below is a snippet that retrieves information about current connections:

SELECT session_id, connect_time, host_name
FROM sys.dm_exec_connections;

This output shows you which sessions are active, when they started, and which machines they originate from. It helps track who is connected and how long they have been running certain tasks.

Also Read: Mastering Schema in SQL

2. User-Defined Views

These types of views in SQL are custom-made queries that focus on specific aspects of your data. They can involve a single table or multiple ones, depending on how much detail you want to reveal. 

Developers often use user-defined views to simplify queries, restrict columns, or even rename fields for clarity. They act as your personal overlays on top of the raw data, with minimal impact on physical storage.

Here is an example of creating a user-defined view.

You can run the snippet below to consolidate essential employee records into a single view:

CREATE VIEW EmployeeBrief
AS
SELECT EmployeeID, FullName, Department
FROM Employees
WHERE IsActive = 1;

This statement defines a view named EmployeeBrief that returns selected columns from an Employees table. By avoiding rummaging through every column in the original table, you keep results focused only on active individuals. Queries against EmployeeBrief are also shorter, which helps you cut down on repetitive code.

Next, you will see several categories of user-defined views, each aiming to solve different problems.

Simple Views

A simple view references just one table and does not include advanced features such as aggregate functions or subqueries. It works best when you want to filter or rename columns without complicating your SQL.

Characteristics of Simple View

• Single Source Table: The definition relies on one base table, making processing queries faster.
• Reduced Complexity: It avoids clauses like GROUP BY or HAVING.
• Easy Updates: You can often insert, update, or delete data through the view if the base table has a primary key.
• Narrow Focus: You choose which columns or rows to display, reducing the chance of exposing irrelevant data.

Example of Simple View
Below is a snippet that returns only active job postings from a table named JobOpenings:

CREATE VIEW ActiveJobs
AS
SELECT JobID, Title, Department
FROM JobOpenings
WHERE IsOpen = 1;

This query creates a view called ActiveJobs that pulls in basic details. You can now query ActiveJobs instead of scanning all positions in the JobOpenings table, which limits clutter and keeps the focus on the rows that truly matter.

Here’s a typical structure for JobOpenings, which shows the columns and their constraints:

When you query the ActiveJobs view, only rows with IsOpen set to 1 appear in the results. This approach keeps the underlying data intact while giving you a streamlined subset for quick reference.

Complex Views

Complex views involve more than one table or include clauses like GROUP BY, aggregate functions, and subqueries. They are suitable when combining or summarizing data from several sources.

Characteristics of Complex View

• Multiple Tables: The definition might involve one or more JOIN clauses.
• Aggregations: You can calculate totals or averages with functions such as SUM or AVG.
• Often Read-Only: Updates can be limited since combining data from different tables complicates write operations.
• Comprehensive Results: The view can serve as a single stop for data from multiple related tables.

Example of Complex View
In the snippet below, the view returns the total client revenue from two tables.

• The ClientRevenue view aggregates total transaction amounts by client name.
• The SUM(t.Amount) function adds up every transaction for each client.
• The GROUP BY clause ensures all amounts are combined per client record rather than returning them separately.

CREATE VIEW ClientRevenue
AS
SELECT c.ClientName, SUM(t.Amount) AS TotalRevenue
FROM Clients c
JOIN Transactions t ON c.ClientID = t.ClientID
GROUP BY c.ClientName;

Below are the two tables that feed into this view. Their relationship hinges on a foreign key, which links each transaction to a matching client.

Clients’ Table

Transactions’ Table

Each row in Transactions includes a ClientID that maps back to Clients, ensuring every transaction belongs to a valid client. The ClientRevenue view then groups those transactions by client and sums each total.

You now have an easy way to access each client’s overall revenue. You can join this view with others or apply further filtering as needed. However, direct inserts or updates often fail because data is drawn from multiple places.

Inline Views

An inline view is a SELECT subquery within the FROM clause of another query. It stays temporary and does not store anything separately, but it simplifies code by breaking down multi-step logic.

Characteristics of Inline View

• Query-Within-a-Query: A subquery acts like a table reference for the outer query.
• Short-Lived: The inline definition vanishes once the outer query completes.
• Simplifies Code: It can replace nested or repeated subqueries with a single pseudo-table.
• Direct Execution: You do not define it ahead of time; you embed it in your main statement.

Example of Inline View
In the snippet below, a subquery calculates average sales per region, then the outer query filters those regions:

SELECT RegionName, AvgSales
FROM
(
  SELECT RegionName, AVG(SalesAmount) AS AvgSales
  FROM RegionalSales
  GROUP BY RegionName
) RegionStats
WHERE AvgSales > 10000;

The inline view, labeled RegionStats, gives each region’s average sales. The main query then retrieves rows only if average sales exceed 10000. This approach avoids creating a permanent view definition.

Materialized Views / Indexed Views

Materialized views and indexed views physically store data from their defining queries. They reduce the time it takes to run certain heavy queries because the results are partially or fully precomputed.

Characteristics of Materialized Views / Indexed Views

• Stored Result Set: Data persists beyond a query’s lifecycle.
• Faster Lookups: Large or complex queries run more quickly due to pre-aggregated info.
• Refresh Cycle: The data needs periodic or manual refreshing to stay up to date.
• Space Overhead: They occupy storage on disk, unlike simple or inline views.

Example of Materialized View / Indexed View
Here is a snippet that stores aggregated sales totals by product:

CREATE MATERIALIZED VIEW ProductTotals
AS
SELECT ProductID, SUM(QuantitySold) AS TotalSold
FROM SalesHistory
GROUP BY ProductID;

The view ProductTotals now has its own stored rows containing product sales. Queries against it run faster than recalculating sums every time. However, you must refresh it (on a schedule or manually) to keep numbers current.

Partitioned Views

Partitioned views stitch together rows from several physical tables into a single, unified result set. These tables can be local to one server or distributed across multiple servers.

Characteristics of Partitioned Views

• Data Distribution: Each table holds a portion of the total dataset (for example, by region or date range).
• Unified Access: A partitioned view treats these separate tables as though they form a single table.
• Scalability Benefits: Splitting data can improve performance for large, segmented datasets.
• Complex Maintenance: You must align each partitioned table with matching columns and data types.

Example of Partitioned View
The snippet below merges quarterly sales tables into one logical set:

CREATE VIEW AllSales
AS
SELECT * FROM Sales_Q1
UNION ALL
SELECT * FROM Sales_Q2
UNION ALL
SELECT * FROM Sales_Q3
UNION ALL
SELECT * FROM Sales_Q4;

The view AllSales then seamlessly shows the complete year's data. This strategy helps maintain large datasets in separate physical segments yet still provides a single point of reference.

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Why Use Views in SQL?

Certain design choices determine how smoothly a database runs. If you find that storing raw data alone is not enough, or your SQL queries keep growing in complexity, there is a structured approach you can adopt instead of managing everything in base tables.

Below are a few reasons to consider using views:

• Simplifying Complex Queries: Reusing a single named definition cuts down on repetitive joins or lengthy logic.
• Enhancing Security: Restricting columns or rows lets you shield sensitive data from unintended exposure.
• Enabling Data Abstraction: Changes in your underlying schema do not break existing queries since the view preserves a stable interface.
• Promoting Reusability: Placing filtering or joining logic in one view means you only write it once instead of duplicating code.
• Supporting Other Use Cases: Creating a focused view also makes tasks like aggregating data for reports or linking distributed tables easier.

Are you interested in knowing how to structure, create, and manage databases using MySQL? upGrad’s free Introduction to Database Design with MySQL course covers ER models, normalization, SQL queries, and relational schema concepts.

Also Read: SQL For Beginners: Essential Queries, Joins, Indexing & Optimization Tips

Next, let’s look at how you can work with the types of views in SQL.

How Do You Create, Alter, and Drop Views?

Design requirements may shift as your database evolves, so having a clear way to introduce, revise, or remove views is fundamental. Each process involves distinct SQL commands that ensure you retain control over how data is exposed and manipulated. 

Despite minor syntax variations between database systems, the main actions stay consistent: you pick a name for the view, define its columns or conditions, and then either update or drop it as you see fit. 

Below is a roadmap for setting up a brand-new view, modifying existing ones, and removing irrelevant ones.

CREATE VIEW Syntax and Examples

The CREATE VIEW command gives you a single reference point for a query that might otherwise be repeated multiple times. You can form a view based on one table or unify data from multiple sources into a logical entity. You are free to filter rows, rename columns, or even combine data from multiple tables. 

Let’s walk through the basic pattern, then see an example.

Below is a snippet that showcases the core syntax:

CREATE VIEW view_name AS
SELECT column1, column2, ...
FROM table_name
WHERE condition;

This template outlines how you declare your view, specify the columns to include, and optionally apply conditions like WHERE to limit rows. For instance, a single-table view might only show records meeting certain criteria, while a multi-table definition could join multiple sources in one place.

You can also rename columns within the SELECT statement to make your view more intuitive. For example:

CREATE VIEW EmployeeOverview
AS
SELECT EmployeeID AS ID, FullName AS Name
FROM StaffRecords
WHERE IsActive = 1;

In this scenario, the table StaffRecords has columns named EmployeeID and FullName, but the view presents them as ID and Name. You also filter out inactive employees by using a WHERE clause. Anyone querying the EmployeeOverview view sees a narrowed, user-friendly version of the table.

Updating a View Definition

Sometimes, a view needs to reflect a shifting reality, like when new columns appear in the base table, or your naming conventions change. Editing a view requires redefining it by default, so many databases offer a CREATE OR REPLACE VIEW statement to streamline the process. 

Below is a snippet that adds a column to an existing view:

CREATE OR REPLACE VIEW EmployeeOverview
AS
SELECT EmployeeID AS ID, FullName AS Name, Department
FROM StaffRecords
WHERE IsActive = 1;

Here, the main difference is the OR REPLACE phrase, which instructs the database to keep the same name (EmployeeOverview) but swap in the new definition. This helps maintain references or permissions already granted on the view. 

However, check whether the new definition aligns with existing queries or applications. Adding columns may be simpler than removing them since dependent queries might break if columns suddenly vanish.

When revising views, consider the following:

• Ensure the base tables still contain the columns you want to add.
• Confirm the WHERE clause and any joins remain logical in your new context.
• Retest any stored procedures or user queries that rely on the original view.

Dropping a View

If a view has outlived its purpose or needs to be replaced entirely, you can remove it. This is done with a simple DROP VIEW command, which stops the database from maintaining a definition under that view name.

Below is the relevant snippet:

DROP VIEW EmployeeOverview;

When you drop a view:

• Any queries or applications pointing to the view will fail if they are not updated.
• Permissions granted directly on that view also vanish since the object no longer exists. 

Make sure no vital dependencies remain before you run this command, especially in production environments. If a view is crucial to ongoing operations, dropping it could break a range of front-end or reporting tasks.

Listing All Views

It can be helpful to see which views exist, especially in larger databases. Different systems offer various ways to look up existing views. Some platforms let you run a statement like SHOW FULL TABLES to spot entries labeled as views, while others use catalog or information schema tables for that purpose.

Below is a short snippet that references system metadata to list every view in your current database:

SELECT table_name
FROM information_schema.views
WHERE table_schema = 'YourDatabaseName';

The result set includes each view’s name, which helps you locate definitions that need review or cleanup. In some environments, you can also retrieve details like the exact SELECT statement used to create each view, which aids in documentation.

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Also Read: Is SQL Hard to Learn? Breaking Down the Challenges and Solutions

Now, let’s look at when you can edit data using the different types of views in SQL.

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When Can You Update, Insert, or Delete Data Through a View?

Some types of views in SQL allow you to run data manipulation language (DML) commands such as UPDATE, INSERT, or DELETE without directly touching the underlying tables. This depends on how the view is defined and whether it includes complex clauses like GROUP BY or references multiple sources. 

In many cases, adjusting the real data through a simplified or filtered perspective can save time. However, not all views qualify, so it's helpful to know exactly when your changes will succeed and how they influence the base tables. 

Conditions for Updatable Views

Views that let you modify underlying data must meet certain requirements. Here is a brief list of conditions:

• No GROUP BY or DISTINCT: Including these features aggregates rows or removes duplicates, which prevents direct row-level updates.
• Single Base Table: The view can only reference one table in its FROM clause. If more tables join in, the view usually becomes read-only.
• Primary Key in Base Table: A key column helps the system identify which exact row you are targeting when you run an update.
• No Derived or Calculated Columns: Columns produced by expressions, functions, or subqueries are not writable.

Updating Data in a View

Some updatable views let you change values in the underlying table without opening multiple objects. If the conditions above are satisfied, your changes automatically flow back to the real data.

Below is a snippet that demonstrates how to update a record through a view. There are no extra columns beyond what’s stored in the base table, so the update works:

UPDATE ActiveJobs
SET Department = 'Technology'
WHERE JobID = 101;

When you run this statement, the system locates the corresponding row in the JobOpenings table (referenced by the ActiveJobs view) and sets its Department to 'Technology'. This approach helps you keep your code centralized if your view has already filtered out irrelevant rows.

Inserting Rows Through a View

When a view includes all the required columns (especially those marked NOT NULL in the base table), you can insert new records by treating the view almost like a direct table reference.

Below is a snippet that adds a fresh job posting via the same ActiveJobs view. Keep in mind that you must include columns for any fields that do not accept null values.

INSERT INTO ActiveJobs (JobID, Title, Department, IsOpen)
VALUES (202, 'Lead Designer', 'Design', 1);

• Because IsOpen is set to 1, this row appears in your ActiveJobs results. 
• In the background, the database writes a new record to JobOpenings, which is the actual store for job data.

Deleting Rows Through a View

The DELETE operation works if the view is directly tied to one base table. In a multi-table scenario, the database cannot determine which physical rows to remove reliably.

Here is a snippet that deletes a row through the ActiveJobs view:

DELETE FROM ActiveJobs
WHERE JobID = 202;

Any row that meets this condition is erased in the JobOpenings table. If your view combined records from more than one table, this command would typically fail or be disallowed.

The WITH CHECK OPTION Clause

Updatable views sometimes need further control so you do not enter data that falls outside their defined conditions. You can add WITH CHECK OPTION to enforce the same WHERE clause used in the view when inserting or updating.

Below is a snippet that illustrates how you might define a view with this clause:

CREATE VIEW ActiveJobsSecure
AS
SELECT JobID, Title, Department
FROM JobOpenings
WHERE IsOpen = 1
WITH CHECK OPTION;

Because of the WITH CHECK OPTION:

• Any changes you make through ActiveJobsSecure must respect IsOpen = 1. 
• If you try to set IsOpen to 0 or omit that field, the database rejects the command.

This mechanism ensures data inserted or updated through the view always stays consistent with your filter logic.\

Also Read: A Guide to Relationships in SQL: One-to-One, One-to-Many, and Many-to-Many

Now, let’s look at why the different types of views in SQL are beneficial.

What Are the Advantages of Different Types of Views?

Some data teams need minimal overhead and immediate visibility, while others prefer higher performance for large aggregations. Each view category responds to a different challenge, which is why selecting the right one can make a significant impact on daily work.

Below are some key advantages different types of views in SQL bring:

• Security and Data Masking: Certain views hide sensitive columns or rows, so you only share what is necessary, reducing the chance of accidental disclosure.
• Performance Gains: Indexed or materialized views hold precomputed data, which can speed up queries that aggregate large volumes of records.
• Data Consolidation: Partitioned and complex views combine multiple tables or data segments, making it easier to track totals or relationships in one place.
• Simplicity in Queries: Views can store repeatable logic in a single definition, so queries become less cluttered and more readable.
• Schema Independence: Stable views reduce disruptions to your existing queries or application code when base tables change structure.

Also Read: Stored Procedure in SQL: How to Create, Executive, Modify, Types & Use Cases

Despite the many advantages of the type of views in SQL, they also come with certain drawbacks, which we’ll discuss now.

What Are the Disadvantages of Different Types of Views in DBMS?

If not planned well, views can hamper performance or add complexity. They can also limit certain updates or hide important row-level details. Recognizing these drawbacks makes it easier to decide whether a specific view approach is right for a given problem.

Below are a few factors that can turn views into a liability:

• Performance Overhead: Complex or nested definitions may slow query execution. Additional processing layers can add noticeable wait times in large systems.
• Maintenance Burden: Changes in base tables require revisiting each dependent view. This added work can create confusion when multiple teams share the same database.
• Limited Updates: Multi-table or aggregate views often restrict direct INSERT, UPDATE, or DELETE operations. The database may reject changes if it cannot pinpoint the exact underlying rows.
• Dependency on Base Tables: A view breaks if a referenced column disappears or certain constraints are modified. Frequent table alterations increase the risk of broken references.
• Confusion with Nested Definitions: Stacking one view on top of another can cause intricate dependencies, making it hard to trace how data transforms from the base table to the final output.

Also Read: SQL Server Architecture: Core Components, Data Management, and Practical Uses

You can avoid these drawbacks by following some of the best practices mentioned below.

What Are the Best Practices for Working with Views?

As a beginner, you might find views a bit overwhelming at first, with their virtual structure, query dependencies, and potential performance challenges. However, once you grasp the different types of views in SQL, you’ll gain a valuable tool to boost your database management skills and efficiency.  

Let’s dive into the key practices for using views.

1. Naming Conventions

Consistent naming conventions improve readability and collaboration when working with views. A transparent naming scheme helps developers understand the purpose of the view and its associated table.

Best practices:

• Use a prefix like vw_ to indicate a view, e.g., vw_ActiveEmployees.
• Include details about the data or purpose in the name, e.g., vw_SalesSummary.
• Avoid overly generic names like View1.

Have a look at the code snippet for how to use the proper naming:

-- Create a view with a clear, descriptive name
CREATE VIEW vw_EmployeeDetails AS
SELECT EmployeeID, FirstName, LastName, Department
FROM Employees
WHERE Status = 'Active';

This approach makes it clear that vw_EmployeeDetails provides information about active employees.

2. Performance Considerations

Based on their design, views can either improve or degrade performance. Simple views are lightweight, while complex or nested views can become slow if not optimized.

Performance tips:

• Avoid including unnecessary columns in the view.
• Limit the number of joins and aggregations.
• Use materialized views for computationally expensive queries.

Understand via the code example:

-- Optimized view with minimal columns and filters
CREATE VIEW vw_DepartmentSales AS
SELECT DepartmentID, SUM(TotalAmount) AS TotalSales
FROM Sales
WHERE SaleDate >= '2025-01-01'
GROUP BY DepartmentID;

This view limits data to recent sales and ensures faster query performance.

3. Security

Views are an excellent way to restrict access to sensitive data. Exposing only specific columns or rows allows you to control what information users can see without modifying the underlying table.

Security tips:

• Grant access to the view instead of the base table.
• Use views to mask sensitive columns, like salaries or personal information.

Look at the code example to have a better understanding:

-- Create a secure view to restrict salary information
CREATE VIEW vw_PublicEmployeeData AS
SELECT EmployeeID, FirstName, LastName, Department
FROM Employees;
-- Grant access to the view, not the table
GRANT SELECT ON vw_PublicEmployeeData TO PublicUser;

Here, the PublicUser cannot access sensitive salary data in the Employees table.

4. Indexing

Indexing can improve the performance of queries that run against views, especially materialized ones. Indexed views (materialized views with indexes) speed up data retrieval for large datasets.

Best tips for indexing:

• Use indexed views for read-heavy operations.
• Ensure the indexed columns match the query’s filter conditions.

Have a look below for a better understanding:

-- Create an indexed materialized view for faster lookups
CREATE MATERIALIZED VIEW mv_IndexedSales AS
SELECT Region, ProductID, SUM(SalesAmount) AS TotalSales
FROM Sales
GROUP BY Region, ProductID;
-- Add an index to the materialized view
CREATE INDEX idx_RegionProductSales ON mv_IndexedSales (Region, ProductID);

5. Limit Nested Views

While creating views that depend on others is tempting, excessive nesting can lead to performance degradation and debugging difficulties. Instead, simplify your queries by reducing dependencies.

Best Practices:

• Avoid chaining views beyond two levels.
• Replace heavily nested views with a materialized view if necessary.

Go through the example below:

-- Nested views can slow down queries
CREATE VIEW vw_RegionSales AS
SELECT Region, SUM(SalesAmount) AS TotalSales
FROM Sales
GROUP BY Region;
CREATE VIEW vw_FilteredRegionSales AS
SELECT Region, TotalSales
FROM vw_RegionSales
WHERE TotalSales > 10000;
-- Better approach: combine into a single view
CREATE VIEW vw_OptimizedRegionSales AS
SELECT Region, SUM(SalesAmount) AS TotalSales
FROM Sales
WHERE SalesAmount > 10000
GROUP BY Region;

Reducing nested layers makes the query more efficient and easier to maintain.

Before presenting insights from databases, you need to accurately assess patterns in data. This is where upGrad’s free Analyzing Patterns in Data and Storytelling course can help you. You will learn pattern analysis, insight creation, Pyramid Principle, logical flow, and data visualization. It’ll help you transform raw data into compelling narratives.

Also Read: SQL Jobs for Freshers: Salary, Career Growth & Opportunities

Next, let’s look at how upGrad can help you learn SQL concepts.

How Can upGrad Help You Learn SQL?

SQL is absolutely vital for data management and analytics, and to excel in it, you need a trustworthy platform. upGrad has empowered more than 10 million learners worldwide, offering industry-relevant programs and free courses tailored for professionals and students.

Along with the courses provided above, here are some additional courses that can help you in your learning journey:

• Introduction to Data Analysis using Excel
• Basics of Inferential Statistics
• Introduction to Tableau
• Case Study using Tableau, Python and SQL
• Data Structures & Algorithms

If you're unsure where to begin or which area to focus on, upGrad’s expert career counselors can guide you based on your goals. You can also visit a nearby upGrad offline center to explore course options, get hands-on experience, and speak directly with mentors! 

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