What is a BigQuery View?

A BigQuery View doesn't store data itself. Instead, it runs the stored query whenever you access the view, pulling fresh results from the underlying data. Views help organise complex logic, simplify reporting, and improve query reusability without duplicating data.

Why Should You Use Views in BigQuery?

Views in BigQuery offer a smart way to organize and simplify your queries. Instead of repeating logic, you can build reusable layers that make data analysis easier and more secure.

• Simplified Querying – Encapsulate complex SQL logic to avoid rewriting the same queries.
• Data Security – Restrict access to sensitive rows or columns without exposing full tables.
• Reusability – Use the same view across multiple reports or dashboards to ensure consistency.
• Abstraction – Interact with a clean, user-friendly version of the data instead of raw tables.

Types of Views in BigQuery

BigQuery provides two types of views to help teams manage query logic and performance based on their needs. These views serve different purposes depending on whether you need real-time access or faster performance.

• Standard Views: These virtual tables run the underlying SQL query whenever the view is queried. They don’t store any data and always return the latest results.
  
  
• Materialized Views: These views store the results of a query and refresh them periodically. They are designed to improve performance by serving precomputed results instead of running the full query each time.

How to Create a View in BigQuery

Creating a view in BigQuery allows you to save a query as a virtual table that can be reused across reports and dashboards. You can create views using SQL or directly within the BigQuery user interface.

• Using SQL (DML statement)
  You can define a view with the CREATE OR REPLACE VIEW statement. 

For example:

CREATE OR REPLACE VIEW `my_project.sales_data.completed_orders_view` AS
SELECT OrderID, CustomerName, OrderDate, TotalAmount
FROM `my_project.sales_data.orders`
WHERE OrderStatus = 'Completed';

• Using the BigQuery UI
  Navigate to your dataset, click the “Create View” option, and enter your SQL query. BigQuery visually distinguishes views from tables with a unique icon for more straightforward navigation.
  
  

Once created, a view can be queried just like a table using a simple SELECT statement.

Common Challenges When Using BigQuery Views

While BigQuery views are useful for simplifying queries and centralizing logic, they also have certain limitations.

These challenges can impact performance, usability, and compatibility with other tools.

• Performance Overhead – Standard views run the underlying query each time they are accessed, which can slow down performance on large datasets.
• Export Limitations – You can’t export data directly from a view to external tools like Google Sheets or Data Studio without additional steps.
  
  
• Nested Views – Building views on top of others can quickly become complex and harder to debug.
• Access Control Issues – Users querying a view must also have access to all underlying tables, which can complicate permission management.
• Query Limitations – Views are subject to query length and complexity limits, which may lead to errors if not managed carefully.

Standard vs. Materialized Views: When to Use Each in BigQuery

Standard views are best when you need real-time access to the most current data without storing results. They’re ideal for dynamic reports and ad hoc analysis, especially when query complexity is low. On the other hand, materialized views are optimal for improving performance on repeated queries. They store precomputed results, making them faster but slightly outdated depending on refresh frequency. 

Choose standard views for flexibility and up-to-date insights, as well as materialized views when performance and cost efficiency are more important than real-time accuracy. Understanding your use case helps decide which view type fits best.

Best Practices for Working with BigQuery Views

To make the most of BigQuery views, it's essential to manage them with performance, clarity, and security in mind. 

The following best practices help teams maintain efficient workflows and ensure reliable reporting.

• Use Clear Naming Conventions – Name views descriptively (e.g., sales_summary_view) so team members can quickly understand their purpose.
  
• Avoid SELECT – Specify only the required columns to reduce data scanned, improve performance, and simplify debugging.
• Leverage Partitioning and Clustering – Partition views by columns like date and cluster by fields like region to speed up queries and lower costs.
• Apply Access Controls – Use IAM roles to restrict access to views instead of raw data, keeping sensitive information secure.
• Audit Permissions Regularly – Review who can access views to maintain compliance and protect data.

Learn More About BigQuery Views

BigQuery views offer a flexible way to simplify SQL logic, improve collaboration, and manage reporting more effectively across your team. Whether building dashboards, modeling data, or maintaining access control, understanding how and when to use views can significantly streamline your analytics workflow.

For a deeper dive into creating, managing, and optimizing views in BigQuery, check out our blog article on BigQuery Views. It includes practical tips, examples, and advanced strategies to help you use views effectively in your data projects.

Discover the Power of OWOX BI SQL Copilot in BigQuery Environments

OWOX BI SQL Copilot helps you write, optimize, and manage SQL queries in BigQuery with ease. It offers smart suggestions, reusable templates, and error detection. It is ideal for analysts and marketers who want to save time, reduce manual effort, and ensure consistent query logic across reports.