The need to modify and update table structures in Snowflake is critical, as user/business requirements shift and data demands evolve constantly, which results in changing data needs. The ability to seamlessly adapt your table schemas without major rework becomes extremely critical. Snowflake's flexibility makes it easy to expand and augment existing tables through simple ALTER TABLE statements. The Snowflake ADD COLUMN command allows new columns and data attributes to be introduced without rebuilding or disrupting tables and relationships.
In this article, we'll cover the ins and outs of adding a column in Snowflake, including syntax, use cases, limitations, tips and tricks—and so much more!
Basics of ALTER TABLE Command
Snowflake provides the ALTER TABLE command for applying structural changes to tables without needing to rebuild or recreate them.
ALTER TABLE provides efficient schema changes with minimal disruption to existing tables, indexes, constraints, grants, and relationships, which enables iterative expansion of tables over time.
Check out the documentation for more in-depth of ALTER TABLE Command
Step-by-Step Guide to Add Column in Snowflake
We will start by outlining the basic steps and syntax on how to add column in Snowflake tables using Snowflake Add column using clear cut examples.
The syntax for adding a column is straightforward:
Let's break this query down:
- ALTER TABLE specifies we want to modify the structure of an existing Snowflake table
- table_name is the identifier of the target table
- Snowflake ADD COLUMN introduces the new data attribute
- new_column_name sets the desired name of the new column
- column_type declares the data type such as VARCHAR, INTEGER, etc
1) Adding a Simple Column using ADD COLUMN in Snowflake
To add a basic new string column called "major" to a table called "students", we can run the following command:
This appends a VARCHAR column to store 255-character string data to the student's table schema.
2) Adding a Column with Default Value
Columns can have default values set when added using the DEFAULT clause:
Now any new rows added to the students table will populate account_status with “active” by default.
3) Adding a Required Column with NOT NULL
For columns that require a value (while inserting data), we can add the NOT NULL constraint:
This forces all rows in the users table to contain a phone number value after alteration.
An important caveat is that the target table must be empty before adding NOT NULL columns. Snowflake cannot backfill NOT NULL without values present.
4) Adding Multiple Columns using ADD COLUMN in Snowflake
We can add multiple columns in a single ALTER TABLE statement like:
This adds both a last name and required address attribute at the same time.
When to Add Column in Snowflake?
The flexibility of ALTER TABLE makes adding column in Snowflake an excellent way to iteratively expand table schemas over time. Adding columns using ADD COLUMN in Snowflake provides a non-destructive way to introduce new data points, metrics, and dimensions to existing tables, which allows changes to reporting, analytics and data integration without rebuilding schemas from scratch. Here are a few good reasons you may need to add column in Snowflake:
1) Adapting to Changing Data Needs
As your priorities as a user shift, new metrics and breakdowns are often required from existing data. Rather than redesigning schemas, new columns can be added to capture emerging needs, such as:
- Adding indicators for user type or segment
- Bringing in new engagement and behavioral traits
- Introducing timeline attributes like order history
Adding columns provides precision to hone in on new dimensions.
2) Enabling New Analytics or Generating Reports
Introducing new analytics/reporting requires access to supporting data elements. Adding columns avoids schema rework to make attributes available like:
- Adding columns for retention or churn calculations
- Bringing in descriptive dimensions for segmentation
- Ingesting new metrics required for extensive analysis/reporting
3) Evolving Schema Design
Early schema design often misses attributes that become useful later on. ALTER TABLE and ADD COLUMN in Snowflake enables easy expansion like:
- Adding temporal columns
- Adding metadata (like author, source and other descriptors)
- Capturing additional descriptive fields
- Incremental expansion of future proofs table design over time
4) Integrating New Data Sources
When bringing in new data from external sources, existing schemas often need expansion to ingest additional fields and attributes provided. Adding columns makes this easy without overhauling schemas.
5) Adding Temporal Data
Time-series analytics requires adding temporal columns (like event, created_at timestamps) to unlock insights. ALTER TABLE provides a simple way to introduce new date-based attributes.
Therefore, rather than requiring painful table rebuilds, ALTER TABLE provides precision when modifying Snowflake tables. Adding columns incrementally future proofs your data model for new use cases over time.
What are the limitations of adding new columns?
While using ALTER TABLE to add column in Snowflake provides powerful schema/table evolution, some limitations do exist, they are:
1) NOT NULL Constraint Requires Empty Table
Adding NOT NULL constraints requires existing tables to be empty first. Attempting to add a NOT NULL constraint to a populated table will result in an error since Snowflake cannot backfill existing rows with a value.
The reason is that existing rows will have NULL values for that column, which violates the NOT NULL constraint being introduced. Snowflake has no way to populate a meaningful value in all existing rows when altering the schema like this.
To add a NOT NULL column, the best practice is to create a new table with the constraint and migrate data into it. For example:
This avoids NOT NULL issues by moving to a new table designed with the constraint up front.
Adding NOT NULL columns to empty tables is possible since new rows will have values populated. But for tables with existing data, NOT NULL requires migration to a new schema first.
2) New Columns Are Added to End of Table
Another limitation is that new columns added via ALTER TABLE are always appended to the end of the table schema. There is no way to directly specify a particular location in the schema to insert the new column.
As you can see, this will add the new middle name column after the last existing column in the table. You cannot place it after the first name column for example.
The only option is to create a new table with columns in the desired order and migrate data:
Now middle name is in the preferred position.
So column order cannot be controlled through ALTER TABLE directly—a new table needs to be created instead.
Pro Tips and Tricks to Add Column in Snowflake
Take advantage of these additional tips and tricks for painlessly adding columns using Snowflake ADD COLUMN :
1) Use IF EXISTS/IF NOT EXISTS
Add IF NOT EXISTS when adding new columns to avoid errors if columns already exist:
Or IF EXISTS when dropping columns to ignore missing ones:
As you can see, this provides control and error handling when making schema changes.
2) Modify Schema Without Blocking Query Execution
ALTER TABLE lets you evolve schema non-destructively without blocking query execution or requiring downtime. Tables remain available throughout additions.
3) Plan Column Changes Up-Front
When possible, plan needed columns upfront even if they won't be populated yet. This avoids excessive ALTER statements down the road.
4) Test Additions First
Thoroughly test column additions and review impacts before applying changes in prod.
Practical scenarios and examples of using Snowflake Add Column
Let's look at some practical use cases for adding columns in Snowflake using a sample Students table:
Example 1—Adding an Email Column
In this example, we need to add an email address column to the Students table to capture contact information:
As you can see, this adds the new Email column to the end of the table schema. Now we can start collecting email addresses for students.
Example 2—Adding a Status Column with Default
Here, we want to add a Status column indicating if a student is active or inactive, with a default of 'active':
The new Status column will be populated with “active” by default for existing rows.
Example 3—Adding Birth Date Column
Suppose we want to capture date of birth information in a new column:
You can see that this adds a column to store date values. We could then backfill historical birth dates or start collecting for new students.
The Students table now has added columns for contact information, status tracking, and date of birth data—all through simple ALTER TABLE statements.
select * from students;
How to Drop a Column in Snowflake ?
While adding columns is useful for expanding schemas, sometimes attributes need removal. Here is how to drop columns using similar ALTER TABLE syntax:
select * from students;
As you can see, you have successfully removed a column from Snowflake table.
Using IF EXISTS for erroless column removal
You can also specify IF EXISTS to suppress errors if the column does not exist:
The column will be removed errorless from the schema and all rows will lose that attribute and data.
Adding columns in Snowflake is a really straightforward and easy task! So with the help of ALTER TABLE and Snowflake ADD COLUMN, you can make table changes seamlessly.
In this article, we covered:
- How ALTER TABLE and Snowflake ADD COLUMN allow painless table changes
- Step-by-step instructions for adding columns using Snowflake ADD COLUMN
- When and why you may need to add column in Snowflake?
- What are the limitations to add column in Snowflake?
- Pro Tips and Tricks to Add Column in Snowflake
- Practical scenarios and examples of using Snowflake Add Columns
- How to Drop a Column in Snowflake ?
Evolving schemas is like remodeling a house. You start with a basic layout for initial needs. But requirements change over time. Rather than demolishing and rebuilding from scratch, remodel incrementally. Altering tables in Snowflake works the same way. You begin with a design for initial use cases. But needs evolve as requirements changes. Instead of rebuilding the entire table, remodel incrementally with ALTER TABLE and ADD COLUMN. Add a column to capture new needs. Expand a table to integrate fresh data.
What command is used to add column in Snowflake?
Snowflake ADD COLUMN command is used to add column in Snowflake.
What is the basic syntax to add column in Snowflake?
ALTER TABLE table_name ADD COLUMN new_column_name data_type;
Can you add multiple columns in one statement in Snowflake?
Yes, you can add multiple columns in a single ALTER TABLE statement by separating them with commas.
How do you add column in Snowflake with a default value in Snowflake?
You can use the DEFAULT clause when adding the column, for example:
ALTER TABLE table ADD COLUMN new_col VARCHAR DEFAULT 'value';
Is it possible to add a NOT NULL column to an existing populated table?
No, adding a NOT NULL column requires the table to be empty first in Snowflake.
Where are new columns added in the table when using Snowflake ADD COLUMN?
New columns are always added to the end of the existing column list.
How can you avoid errors if a column already exists when adding it?
Use IF NOT EXISTS, for example:
ALTER TABLE table ADD COLUMN IF NOT EXISTS column_name;
When would you need to add a new column in Snowflake?
When new data attributes are needed, changing business needs require new metrics, or to integrate data from new sources.
How do you drop a column in Snowflake?
Use ALTER TABLE table_name DROP COLUMN column_name;
Can you remove a column if you're unsure whether it exists?
Yes, use IF EXISTS, e.g. DROP COLUMN IF EXISTS column_name;
Does ALTER TABLE and Snowflake ADD COLUMN command require blocking queries or downtime?
No, ALTER TABLE and Snowflake ADD COLUMN command is non-destructive and doesn't block queries.
Should column changes be planned ahead of time where possible?
Yes, it's best to plan needed columns upfront to avoid excessive ALTER statements later.
Does ALTER TABLE modify or recreate the table when adding columns?
No, ALTER TABLE simply modifies the schema, it does not recreate or rebuild the table.