Snowflake DDL Commands, Tables, and Views

Snowflake is a leading data warehousing platform that companies use for efficient data storage and data analysis. It offers innovative features like data cloning, data sharing, data integration, and analytics.

Snowflake administrators use specific DDL commands to manage different object types in data warehouses. The objects can include users, tables, databases, and views. DDL commands such as CREATE, ALTER, DROP, and DESCRIBE enable administrators to modify the structure of these objects.

This tutorial provides more details about Snowflake DDL functions and how to use them effectively. 

Table of Content:

• Snowflake DDL Commands
• Snowflake DDL Functions
  • Create a Table
  • Add a Column
  • Drop a Column
  • Rename a Column
  • Rename a Table
  • Drop a Table
  • Duplicate a Table
  • Create a View
  • Drop a View
  • Create an Index
  • Drop an Index
  • Add a Default Value to a Column
  • Remove a Default Value from a Column
  • Add a NOT NULL Constraint
  • Remove a NOT NULL Constraint
  • Alter a Sequence
• Frequently Asked Questions and Answers

Snowflake DDL Commands - Overview

You can use Snowflake DDL commands to perform many account-level and session operations. For example, you can use these commands to initialize variables, set parameters, and initiate transactions.

Every DDL command has an object type and an identifier. You can group DDL commands into multiple categories, including:

• User and security DDL commands
• Warehouse and resource monitor DDL commands
• Data loading/unloading DDL commands
• Database, schema, and share DDL commands
• Machine Learning (ML) model DDL commands
• Data pipeline DDL commands

Now that you understand what Snowflake DDL commands are.

Want to enhance your skills to become a master in Snowflake Certification, Enroll in our Snowflake Training Certification Course

Snowflake DDL Functions - A Deep Dive

In this section, we will dive into how to use Snowflake DDL functions to create tables, add columns, rename and drop tables, and more.

How to Create a Table in Snowflake

As you know, we create tables in databases to store and analyze data. It helps store the data in a specific structure. Also, we can rapidly retrieve and analyse data using tables.

The code below shows how to create a table in Snowflake.

Create table users1 (
id1 integer default id1_seq.nextval,
name1 varchar(1000),
preferences string,
created_at timestamp
)

In the above code, columns are separated by commas within the parentheses. The minimum necessary fields for the column definitions are the data type and column name. The ‘id1’ column contains another field that uses an auto-increment value.

The code below defines ‘default values’ and ‘not NULL constraints’.

create table users1 (
Id1 integer default id1_seq.nextval,
name1 varchar(1000) not null,
active boolean default true;
);

You can create temporary tables to use during the session. This helps break your analysis into manageable, smaller parts.

Create temporary table1 active_users (
id1 integer default id_seq.nextval1,
name1 varchar(1000) not null,
active boolean default false
);

You can create transient tables in Snowflake that retain data after a session ends and offer some features of permanent tables, but they have limited data recovery and security. Temporary tables persist only within a session and do not provide enhanced data recovery or security.

Create transient table inactive_users (
id1 integer default id_seq.nextval,
name1 varchar(100) not null,
active boolean default false
);

In short, Snowflake allows you to store data in different types of tables, like transient tables, temporary tables, etc.

How to Add a Column in Snowflake

Adding columns in Snowflake helps you better manage or organize data. Let’s learn how to add new columns to your existing Snowflake tables.

You can use the ‘ALTER TABLE’ command to add columns to Snowflake tables.

For example, if we want to add a column named “customer_id smallint”, the code below can help.

alter table product1
Add customer_id smallint;

By using the “ALTER TABLE” command, you can add columns with default values as shown below.

alter table product1
add column customer_id smallint default 3;

You can add the string column with the NOT NULL constraint using the code below.

alter table product1
add type varchar(100) not null;

You can use the code below to add multiple columns

alter table product1
add
Customer_id small int default 3;
type varchar(100) not null;

The ALTER TABLE command lets you add multiple types of columns.

[Also Read: Snowflake Tutorial]

How to Drop a Column in Snowflake

In Snowflake tables, you will have rows and columns to store data in order. When you no longer need any data, you can delete it. To delete that data, you need to delete those columns.

Let’s learn how to drop a single column or multiple columns.

In Snowflake, you can drop columns using the “ALTER TABLE’…DROP COLUMN” command.

• Dropping a single column in Snowflake

alter table product1
drop column description1;

• Dropping multiple columns in Snowflake

alter table product1
drop column price1, description1;

How to Rename a Column in Snowflake

While storing data in database columns, you can change column names when needed.

Let’s look into how to rename a column.

To rename a column in Snowflake, you can use the "ALTER TABLE ... RENAME COLUMN" command.

The syntax and example are shown below.

• Syntax
  
  

  alter table table_name1 rename column old_name1 to new_name1

  
  Example
  

  alter table products1 rename column products_category1 to products1;

How to Rename a Table in Snowflake

In Snowflake, we use tables for storing data. While creating a table, you assign a name to that table. If you want to change that table name, Snowflake allows you to do so.

In Snowflake, use 'ALTER TABLE ... RENAME TO' to rename a table.

• Syntax:
  
  

  alter_table old_table_name1 rename to new_table_name1;
  alter_table sessions_db2 rename to sessions_db_3;

So, you can use the “ALTER TABLE .. RENAME TO” command to change the name of a table.

How to Drop a Table in Snowflake

You can drop a table in Snowflake using the DROP command. Snowflake provides the ‘undo’ option for the ‘drop’ command.

Drop table user1;

If you want to restore the table, you can restore it using the ‘undrop table’ command.

undrop table users;

You can drop and restore tables in Snowflake using the ‘drop’ and ‘undrop’ commands.

How to Duplicate a Table in Snowflake

In Snowflake, we can duplicate a table using the’ COPY CLONE ’ command. We primarily use them to create tables, schemas, and databases.

To duplicate a table, you can apply various methods. You can choose the right method based on the needs.

If you want to copy both the data and the table structure, use the method below.

create table sessions1_copy clone sessions1;

If you want to copy only specific columns from many tables into a new table with a specific dataset, you can use the code below.

create table users1_sessions12_rpt as
select*
u1.name,
s.start_date1 as session_start_date,
s.end_date1 as session.end_date1,
from sessions1 s
left join user_sessions us on s.id1 = us. session.id1
left join users_1 u on us. user_id1 = u.id1
where u1.active = true;

If you want to copy only the table structure—not the data—then you can use the code below.

create table users1_copy like users1;

Cloning data objects replicates the data, structure, and properties of a source table, but not the loading history.

How to Create a View in Snowflake

In Snowflake, you can create views to secure files and set access restrictions on them. Let’s discuss creating views in Snowflake.

You can use ‘CREATE VIEW’ to grant restricted access to selected data in Snowflake.

If you want to create a view, you can use the ‘CREATE VIEW’ command.

create view view_name
as select_statement;

Example:

create view category1_2_products1_u as
select *
from products1
Where category1 = 2;

You can create a view to restrict read access to only particular columns using the code below.

Create view category_products_basic_u as
select
category1,
name1,
unit_price1
from products1

The code below helps you create a view displaying the top 12 products by sales value.

create view top_12_product1_v as
select
top 12 p1.name1,
p1.category1,
p1.unit_price1,
ps1.quantity_sold1,
p1.unit_price1 * ps1.quantity_sold1 as sold_value1
from product1 p1
left join products_sold1 ps1 on p1.id = ps1.product_id
order by sold_value1 desc;

In summary, we create views to display the selected columns and rows across multiple tables.

How to Drop a View in Snowflake

A view is a database object that displays selected columns or rows from multiple tables.

If you no longer want to keep views, you can drop them.

For dropping a view, you can use the ‘DROP VIEW’ command.

drop view category_2_products1_u;

So, you can drop unwanted views in Snowflake using the ‘DROP VIEW’ command. 

How to Create an Index in Snowflake

Snowflake does not support indexes, but it uses a clustering key instead, which organizes specific columns and improves query performance, unlike traditional indexes that use separate data structures.

Here, we will learn to create the Clustering key in Snowflake.

Rather than creating or dropping an index in Snowflake, you can use the clustering key to achieve query performance. To create the clustering key, use ALTER TABLE…CLUSTER BY command.

• Syntax
  
  

  alter table table_name cluster by (column1, column2, …, columnN);

   

• Example
  
  If you need to create a cluster on a single column, the code below can help you.
  

  alter table active_users1
  cluster by (id1)

  If you need to create a cluster across multiple columns, the code below can help.

  alter table active_users1
  cluster by (id1, active1)

Importantly, you should be careful when using clustering keys. The clustering key must be used only when you are handling tables with the following properties.

• Data from tables is filtered frequently
• Tables containing a vast amount of data

Thus, clustering enables you to co-locate data and is suitable for large tables where ordering is suboptimal.

How to Drop an Index in Snowflake

Since Snowflake does not support indexes, it is well-suited for random queries.

Let’s discuss dropping clustering keys in Snowflake.

In some situations, you can use clustering keys as a substitute for Indexes. For dropping a clustering key, you can use the ‘ALTER TABLE....DROP CLUSTERING KEY’ command.

• Syntax
  
  

  alter table table_name drop clustering key;

   

• Example:
  
  

  alter table active_users drop clustering key;

 

In Snowflake, if you don’t need a clustering key any longer, you can drop it.

How to Add a Default Value to a Column in Snowflake

Let’s learn how to alter the existing columns and add default values to the columns.

You can use the 'ALTER' command to add default values in Snowflake. ALTER commands also change table and column names.

In Snowflake, you cannot add default values to existing columns directly unless a sequence object provides the default value.

You can use the command below to change the default for the column that already has a sequence.

ALTER TABLE <table_name> ALTER <column_name> SET DEFAULT

• Example:
  
  

  alter table products1
  alter id1
  set default id1.seq.
  nextval1;

Thus, we can use the ALTER command to add default values to columns.

How to Remove a Default Value from a Column in Snowflake

In Snowflake, we can remove default values from a column using the ’ ALTER TABLE ’ command.

The following example shows the use of the ‘ALTER TABLE’ command in Snowflake.

ALTER TABLE <table_name> ALTER <column_name> DROP DEFAULT

Example:

alter table products
alter id1
drop default1;

The ’ ALTER TABLE ’ command allows you to remove default values from a particular column.

How to Add a NOT NULL Constraint in Snowflake

In Snowflake, adding a ‘NOT NULL Constraint’ is performed using the ‘Alter Table’ command.

Let’s understand the syntax of ‘ALTER TABLE’ and use it to add a ‘NOT NULL Constraint’.

Know that adding a ‘NOT NULL Constraint’ to  Snowflake tables can lead to inconsistency. Someone may forget to add validation, accidentally remove it, or avoid validation in the console and insert NULLs.

You must perform data validation in the application layer to avoid those inconsistencies. If you are validating the NULLs on the database layer, it increases protection.

To apply NOT NULL for the column in Snowflake, you can use the ALTER TABLE <table_name> ALTER <column_name>command. It helps you reiterate the column definition and add the NOT NULL attribute.

alter table products1
alter type not null;

How to Remove a NOT NULL Constraint in Snowflake

In Snowflake, we can remove the ‘NOT NULL’ constraint using the “ALTER TABLE” command.

We can use the following command to remove the ’ NOT NULL ’ constraint.

ALTER TABLE <table_name> ALTER <column_name> DROP 

You can restate the column definition by adding the ‘NOT NULL’ Attribute.

alter table products1
alter category drop not null;

How to Alter a Sequence in Snowflake

For altering the sequence, we can use ‘Alter Sequence’ or ‘Increment’ commands.

In Snowflake, you can create a number sequence using the “create sequence’ command. After creating the sequence, you can alter it later.

By default, auto-incrementing columns start at 1. Sometimes, you may want them to begin at a different number or increment by a different amount. These numbers are called “sequences.”

Here is the syntax for creating Sequences.

Create sequence sequence_name

start = number1;
increment = number1;

• Examples
  • Here is the code for creating a sequence for odd numbers
    
    

    start = 1;
    increment = 2;

     

  • Here is the code for creating a sequence for positive numbers
    
    

    start = 0;
    increment = 1;

For altering sequences, we can use the “ALTER SEQUENCE” command.

To change the sequence number, use the command below.

alter sequence odd numbers
set increment  = 11;

To set a Comment for the Sequence, you can use the command below.

alter sequence odd_numbers
set comment = ‘odd dozens’

To rename the Sequence, use the following command.

alter sequence odd_numbers rename to odd_numbers_dozens;

In summary, we can create a sequence to print numbers in order in Snowflake. When you want to change the order, you can alter the sequence.

The Top 40+ Best BigQuery Interview Questions & Answers 2025 article can help you understand key concepts and prepare for interviews.

Frequently Asked Questions and Answers

1. How does Snowflake differ from other traditional databases?

Ans: Snowflake separates storage and compute, enabling independent scaling operations unlike traditional databases. As a result, it increases the platform's security and performance compared to traditional databases.

2. What is the difference between the DROP and TRUNCATE commands in Snowflake?

Ans: The DROP command deletes the entire object, whereas the TRUNCATE command removes all rows from a table but keeps the table structure intact.

3. What cloud platforms does Snowflake support?

Ans: You can run Snowflake on Microsoft Azure, AWS, and Google Cloud Platform.

4. Can I learn Snowflake easily as a beginner?

Ans: Yes, you can learn Snowflake quickly, regardless of your technical background. Gaining a basic understanding of SQL and databases will simplify your learning process.

5. Are there any additional learning resources to learn Snowflake?

Ans: You can use the following e-learning resources to strengthen your knowledge of Snowflake.

• Snowflake Tutorials
• Snowflake Interview Questions
• Snowflake Sample Resumes
• Snowflake Quiz

6. What are the use cases of Snowflake?

Ans: You can use Snowflake for the following:

• Data Warehousing
• Business Intelligence
• Data Engineering
• Real-time Analytics

Conclusion

Let’s conclude! In this tutorial, you have walked through the key Snowflake DDL commands and how to use them with examples. No doubt, these DDL functions play a key role in defining and managing database structures.

If you want to explore more about Snowflake DDL commands, you can enroll in a Snowflake course at MindMajix. Completing the training enables you to use Snowflake DDL commands and manage Snowflake data warehousing environments effectively.