CREATE TABLE AS statement
PostgreSQL CREATE TABLE AS statement is used to create a table from an existing table by copying columns of a current table. It is important to note that building a table will fill the new table with records from the existing table (based on the SELECT operator).

The syntax for CREATE TABLE AS in PostgreSQL
CREATE TABLE new_table AS
SELECT expressions
FROM existing_tables
[WHERE conditions];
Parameters and arguments of the statement
- table_name – The name of the table you want to create.
- expressions – Columns from existing_tables that you want to create in new_table. Will move the definitions of the columns from these columns to the new_table you made.
- existing_tables – Existing tables from which you can copy the column definitions and related entries (as suggested by WHERE).
- WHERE conditions – Optional. Requirements that must meet to copy records to the new_table.
Note:
- will copy column definitions from existing_tables to the new_table.
- new_table will be filled with entries based on conditions in the WHERE proposal.
Take the example of PostgreSQLCREATE TABLE, which shows how to create a table by copying all columns from another table.
CREATE TABLE current_inventory AS
SELECT *
FROM products
WHERE quantity > 0;
In this example, we will create a new table named current_inventory, including all columns from the products table. If the products table has records, fill the new current_inventory table with descriptions returned by the SELECT operator. Meanwhile, all entries from the product table with a number greater than 0 will be inserted into the current_inventory table when it is created.
Next, consider CREATE TABLE AS, which shows how to create a table by copying selected columns from multiple tables.
For example:
CREATE TABLE current_inventory AS
SELECT products.product_id, products.product_name, categories.category_name
FROM products
INNER JOIN categories
ON products.category_id = categories.category_id
WHERE products.quantity > 0;
This example will create a new table named current_inventory based on the column definitions from the products and categories tables. Also, the new current_inventory table will only add entries that satisfy the SELECT operator conditions.
PostgreSQL: Creating Tables with Constraints | Course
About Enteros
IT organizations routinely spend days and weeks troubleshooting production database performance issues across multitudes of critical business systems. Fast and reliable resolution of database performance problems by Enteros enables businesses to generate and save millions of direct revenue, minimize waste of employees’ productivity, reduce the number of licenses, servers, and cloud resources and maximize the productivity of the application, database, and IT operations teams.
The views expressed on this blog are those of the author and do not necessarily reflect the opinions of Enteros Inc. This blog may contain links to the content of third-party sites. By providing such links, Enteros Inc. does not adopt, guarantee, approve, or endorse the information, views, or products available on such sites.
Are you interested in writing for Enteros’ Blog? Please send us a pitch!
RELATED POSTS
How to Transform Financial Operations with Enteros Database Software and Growth Intelligence
- 10 June 2026
- Database Performance Management
Introduction The financial services industry is experiencing unprecedented digital transformation. Banks, insurance providers, fintech organizations, investment firms, and financial institutions are rapidly modernizing their technology infrastructures to meet evolving customer expectations, regulatory requirements, and competitive market demands. Modern financial organizations now rely on: Digital banking platforms Mobile financial applications Payment processing systems Risk management platforms … Continue reading “How to Transform Financial Operations with Enteros Database Software and Growth Intelligence”
How to Enable Intelligent AI Growth with Enteros Database Performance Management and Operational Intelligence
Introduction Artificial Intelligence (AI) is transforming industries across the globe. From generative AI applications and large language models (LLMs) to predictive analytics, intelligent automation, and machine learning platforms, organizations are investing heavily in AI technologies to improve productivity, accelerate innovation, and drive business growth. Modern AI ecosystems now support: Generative AI platforms Machine learning environments … Continue reading “How to Enable Intelligent AI Growth with Enteros Database Performance Management and Operational Intelligence”
How Real-Time Database Observability Accelerates Digital Transformation Initiatives
Digital transformation has become a strategic priority for organizations seeking to remain competitive in an increasingly data-driven world. Enterprises across industries are investing in cloud-native technologies, artificial intelligence, automation, advanced analytics, and modern applications to improve operational efficiency, enhance customer experiences, and drive innovation. However, successful digital transformation requires more than adopting new technologies. Organizations … Continue reading “How Real-Time Database Observability Accelerates Digital Transformation Initiatives”
Leveraging AI and Predictive Analytics for Autonomous Database Performance Management
In today’s digital-first economy, organizations depend on high-performing databases to support critical business applications, customer experiences, analytics platforms, and operational systems. As enterprises continue adopting cloud-native architectures, multi-cloud deployments, microservices, and real-time digital services, database environments are becoming increasingly complex and difficult to manage. Traditional database performance management approaches often rely on manual monitoring, reactive … Continue reading “Leveraging AI and Predictive Analytics for Autonomous Database Performance Management”