Snowflake User-Defined Types: Giving Your Schema a Face-Lift

Data platforms are only as good as the contracts they enforce. Developers can build elegant pipelines, meticulously layered architecture, and robust data quality processes, and still end up with garbage data. One of the driving causes of garbage data is a schema that does not reflect the business reality. That’s where most teams hit a wall: they’ve got NUMBER columns that could mean anything, VARCHAR fields holding data that should follow stricter rules, and no clean way to communicate data intent across tables, teams, and time.

Insert the latest game-changer from Snowflake: User-Defined Types, or UDTs. With a UDT, a Snowflake developer can create a custom data type rooted in existing Snowflake types but carrying the meaning and constraints of the business domain. Think of a UDT as a way to make your schema self-documenting by havijng column types for age or address, which tells users more than NUMBER(3,0) or OBJECT ever will.

In this post, I review the fundamentals of UDTs, explore the technical requirements needed to make your first UDT, and walk through real-world patterns that make UDTs worth adopting. Whether you’re a data engineer trying to tighten up your schemas or an architect looking for better ways to enforce consistency, there’s something here for you.

What Are User-Defined Types (And Why Should You Care)?

At their core, User-Defined Types are schema-level objects that let developers define new data types based on existing Snowflake data types. Instead of inventing data types from scratch, developers create named aliases that carry specific type information and can be reused wherever types are used: column definitions, function signatures, procedure parameters, and cast expressions.

Why are UDTs important for the evolution of data architecture? Consider a scenario most developers have lived through. A developer has a customers table with a postal_code column defined as VARCHAR. In a separate table, orders, you also have a column called zip_code of type VARCHAR. Furthermore, your shipping_address table has a postal code embedded as a semi-structured VARIANT. With all of these, developers have no shared contract, no guarantee that fields follow the same rules, and no way for a new team member to look at the schema and understand that the fields are all supposed to represent the same type of data.

With UDTS, you define postal_code once as a data type within your Snowflake org, and use it everywhere. If someone looks at any table in your schema, they immediately understand what that column represents and what rules it follows. This DRY (don’t repeat yourself) approach is another way Snowflake is bridging the gap between data developers and software developers, adopting more software engineering methodologies and applying them to the data world.

While such a narrow use case can help create data contracts in a space that has traditionally been underserved by data platforms, UDTs also enable grouping related fields into a single, logical column using structured OBJECT types. Instead of scattering street, city, state, and postal_code across four columns (or worse, multiple tables), you can define an address data type that holds all of these related fields together. Let’s explore what that looks like.

Getting Started: Creating Your First UDT

To kick things off, I’ll start with a simple example. To create a User-Defined Type, a developer would use the CREATE TYPE statement like below:

CREATE TYPE age AS NUMBER(3,0);

As simple as that, I have created a custom data type called age that maps to NUMBER(3,0), a number with at most three digits and no decimal places. With the creation of the new data type, I can now use it in my table definitions:

CREATE TABLE employees (
    emp_id VARCHAR NOT NULL,
    emp_name VARCHAR(100),
    emp_age age
);

Any developer who looks at the employees table will know that emp_age isn’t just a number; it is a value that carries meaning.

As developers create new UDTs, there is one prerequisite to keep in mind: privileges. To create a UDT in a schema, the user must have the CREATE TYPE privilege on that schema. Snowflake administrators can grant the privilege by using:

GRANT CREATE TYPE ON SCHEMA my_db.my_schema TO ROLE data_engineer_role;

Once a developer has been granted the necessary privilege and the UDT has been defined, inserting data works exactly the way one might expect:

INSERT INTO employees VALUES ('E001', 'Jane Doe', 32);

Snowflake handles the coercion from the literal 32 to the age type seamlessly. The value fits within NUMBER(3,0), so there are no errors or invalid values for data type issues. If a user or system tries to insert a value such as 1000 or 32.5, Snowflake would return an error indicating that the value is not of the correct data type.

Beyond Scalars: Structured Object UDTs

Simple scalar types are useful, but the real power of UDTs shows up when you combine a UDT with structured OBJECT types. This coupling of types allows developers to model complex, real-world entities as first-class data types in any schema. Take the example below:

CREATE TYPE address AS OBJECT (
    street VARCHAR(100)
    , city VARCHAR(50)
    , state_abbr CHAR(2)
    , postal_code CHAR(10)
);

Now that there is a reusable data type that encapsulates everything an address needs to be valid, it can be used in a table:

CREATE TABLE customers (
    cust_id VARCHAR NOT NULL,
    cust_name VARCHAR(100),
    cust_address address
);

Inserting data into a structured UDT column requires the developer to cast the object to the UDT. There are two approaches that developers can take, and both are worth knowing:

Approach 1: OBJECT constant with cast

INSERT INTO customers (cust_id, cust_name, cust_address)
    SELECT
        '1000'
        , 'Acme Corp'
        , {
            'street': '101 Bikini Bottom'
            , 'city': 'Ocean Floor'
            , 'state_abbr': 'CA'
            , 'postal_code': '90210'
        }::address;

Approach 2: OBJECT_CONSTRUCT with cast

INSERT INTO customers (cust_id, cust_name, cust_address)
    SELECT
        '1001'
        , 'Widgets Inc'
        , OBJECT_CONSTRUCT(
            'street': '101 Bikini Bottom'
            , 'city': 'Ocean Floor'
            , 'state_abbr': 'CA'
            , 'postal_code': '90210'
        }::address;

While both approaches work just fine, the OBJECT constant syntax is cleaner for hardcoded values; OBJECT_CONSTRUCT is more flexible when a developer is building objects dynamically from other columns or expressions.

Once the data is inserted into the table, querying individual fields is straightforward using the colon operator:

SELECT
    cust_id
    , cust_name
    , cust_address:city
    , cust_address:postal_code
FROM customers;

Developers now have a query that returns clean, extracted values from a structured data type without needing PARSE_JSON, lateral flattening, or other “gotchas”. Direct field access on a well-defined type, creating an experience that makes schemas easier to work with at scale.

Casting, Coercion, and the Gotchas That’ll Get Ya

In this section, I will explore time-saving tips around UDT-specific behaviours for type casting and coercion. These concepts are straightforward once there is an understanding of them, but can be confusing without.

Explicit Casting

A value can be cast to a UDT if it can be cast to the UDT’s base type. Going the other direction, a UDT value can be cast to any type that its base type can be cast to:

-- Cast a literal to the age type
SELECT 25::age;

-- Cast an age value to VARCHAR
SELECT 25::age::VARCHAR;

This chaining works because Snowflake resolves the UDT to its base type and then applies the normal casting rules. Nothing surprising here.

Implicit Coercion

Implicit coercion is where things get interesting. UDT values coerce to their base types implicitly in operations. Such implicit coercion means arithmetic, comparisons, and other expressions work exactly as they would with the base type:

CREATE TABLE test_ages (a age, b age);
INSERT INTO test_ages VALUES (10, 20);

SELECT a + b AS result,
       SYSTEM$TYPEOF(a + b) AS type
  FROM test_ages;

The result is 30, and the type is NUMBER(4,0), not age. The UDT coerced to its base type for the operation. This behavior is important to internalize: operations on UDT values produce base-type results, not UDT results.

The Set Operator and Conditional Expression Trap

Set operators and conditional expressions can trip developers up if they don’t understand them. When using set operators like UNION, INTERCEPT, or EXCEPT, or conditional expressions like CASE, IFF, COALESCE, or NVL with UDT values, Snowflake resolves to the common base type. The result is not a UDT.

I’ll attempt to make this concept concrete. Create two UDTs that share the same base type:

CREATE TYPE us_zipcode AS VARCHAR;
CREATE TYPE uk_postcode AS VARCHAR;

Now use the new UDTs in a conditional expression:

SELECT IFF(TRUE, '90210'::us_zipcode, '10006') AS result,
       SYSTEM$TYPEOF(IFF(TRUE, '90210'::us_zipcode, '10006')) AS type;

The result type? VARCHAR, not us_zipcode. The UDT information is gone. If a developer needs to preserve the UDT, they must explicitly cast the entire expression:

SELECT IFF(TRUE, '90210'::us_zipcode, '10006')::us_zipcode AS result,
       SYSTEM$TYPEOF(IFF(TRUE, '90210'::us_zipcode, '10006')::us_zipcode) AS type;

Now it returns MYDB.MYSCHEMA.US_ZIPECODE as the type. The pattern holds for CASE expressions, COALESCE, and set operators. If developers want UDT output, they need to cast the final result.

This same casting behavior applies when mixing compatible UDTs. A CASE expression that returns either a uk_postcode or a us_zipcode will resolve to VARCHAR:

SELECT CASE
         WHEN TRUE THEN 'SW1A 0AA'::uk_postcode
         ELSE '90210'::us_zipcode
       END AS result,
       SYSTEM$TYPEOF(CASE
         WHEN TRUE THEN 'SW1A 0AA'::uk_postcode
         ELSE '90210'::us_zipcode
       END) AS type;

Result type: VARCHAR. To get uk_postcode, wrap the whole thing in a CAST:

SELECT CAST(
         CASE
           WHEN TRUE THEN 'SW1A 0AA'::uk_postcode
           ELSE '90210'::us_zipcode
         END AS uk_postcode
       ) AS result;

SYSTEM$TYPEOF now becomes a developer’s best friend when debugging the above situations. When something downstream breaks because a function expects a UDT but receives a base type instead, this is almost always the reason.

UDTs in Functions, Procedures, and Overloading

UDTs integrate with the broader Snowflake programming model, but there are a few nuances worth calling out.

UDTs as Function Arguments and Return Types

Developers can use UDTs as argument types and return types in UDFs and stored procedures. This functionality is great for enforcing data contracts in your function signatures. A function that accepts an age parameter communicates something different than one that accepts NUMBER(3,0).

There’s one critical rule for return types: if a UDT is specified as the return type of a SQL UDF or Snowflake Scripting stored procedure, the return value must be explicitly cast to the UDT in the function body. Snowflake won’t cast the return value automatically:

CREATE OR REPLACE FUNCTION format_age(input_age age)
  RETURNS age
  LANGUAGE SQL
  AS
  $$
    SELECT input_age::age
  $$;

Skip that cast, and Snowflake will throw an error. It’s a small detail, but it catches developers off guard the first time.

Non-SQL Languages

When writing UDFs or procedures in Python, Java, or other non-SQL languages, UDTs are treated as their base types. There’s no special UDT handling in the Python or Java runtime — a parameter of type age comes in as a regular number. This behavior is pragmatic; it means you don’t need UDT-aware libraries in your handler code. But it also means the UDT boundary is enforced at the Snowflake SQL layer rather than within procedural code.

Function Overloading

UDTs and their compatible base types can be used for function overloading. Developers can define two functions with the same name, where one accepts an age argument, and another accepts a NUMBER(3,0) argument. Snowflake will resolve the correct function based on the argument type at call time. Function overloading is a powerful pattern for building type-safe APIs within a data platform.

Real-World Patterns and When (Not) to Use UDTs

Now that I’ve covered the mechanics, let’s talk about where UDTs deliver real value and where you should think twice.

Where UDTs Shine

Domain-specific type standardization. This standardization is the primary use case. If an organization has concepts that appear across multiple tables, such as customer IDs, product codes, currency amounts, and postal codes, defining them as UDTs establishes a single source of truth for how those fields are defined. Change the definition once, and every table that uses the type is aligned (with the caveats I’ll cover below).

Structured entity modeling. The address example isn’t just a demo, it’s a pattern. Anywhere there is a cluster of related fields that always travel together (addresses, contact info, geographic coordinates, monetary amounts with currency), a structured OBJECT UDT keeps them cohesive. It reduces column sprawl and makes for a more intuitive schema design.

Self-documenting schemas. When a new engineer joins the team and looks at table definitions, UDTs tell them what the data means, not just its shape. age communicates something that NUMBER(3,0) doesn’t. address communicates something that five separate VARCHAR columns don’t. This is an underrated use case that can be a huge benefit for organizations that need to ramp up new developers quickly.

Function signature contracts. Using UDTs in UDF and procedure signatures makes a data platform’s API layer more expressive. A function that takes a us_zipcode is making a statement about what it expects, and Snowflake can enforce that at the type level.

Where to Be Careful

Schema evolution isn’t supported. Schema evolution is the big gotcha. If data sources change frequently and developers rely on schema evolution to automatically add columns, UDTs won’t play well with that workflow. Unsupported schema evolution is a meaningful limitation for ingestion-heavy pipelines where source schemas are volatile.

Drop-and-recreate for changes. Developers can’t use ALTER TYPE to change the definition of a UDT. To modify one, developers have to drop it and recreate it. When dropping and recreating a UDT, SQL statements that operate on columns using the type may start returning errors. Functions and procedures that reference the type will also break and need to be dropped and recreated. This behavior means UDT changes require coordination to perform an essentially schema migration, which Snowflake will hopefully address over time.

The coercion behavior requires discipline. As I showed in the casting section, operations on UDT values silently resolve to base types. If downstream logic depends on the result being a UDT, developers need to use explicit casts everywhere. In complex queries with multiple CTEs and transformations, it’s easy to lose the UDT type along the way without realizing it. Build the habit of using SYSTEM$TYPEOF during development to verify types at each stage.

Column alterations. The ALTER TABLE . . . ALTER COLUMN command can change a column from a UDT to a compatible Snowflake type and vice versa. Using this approach gives developers an escape hatch if they need to move away from a UDT, but it also means anyone with the right privileges could inadvertently strip the UDT from a column. Governance around type changes matters.

Conclusion

User-Defined Types are one of those features that don’t make much noise but can fundamentally improve how a business’s data platform communicates intent. UDTs sit at the intersection of data quality, schema design, and developer experience, three things that every mature data organization cares about deeply.

The basics of UDTs are approachable. Create a custom type. Use the UDT in a table. Insert data into the table. The learning curve is gentle, and the immediate payoff is a more readable, self-documenting schema. As developers dive deeper, the integration with structured OBJECT types, UDFs, stored procedures, and function overloading opens up patterns that make a data platform more expressive and data contracts more enforceable.

But UDTs aren’t a magic wand. The lack of schema evolution support, the drop-and-recreate lifecycle, and the implicit coercion behavior all require intentional design decisions. Developers and architects need to consider where UDTs add value within a specific architecture and where the overhead isn’t justified.

My recommendation? Start small. Pick a handful of domain concepts that appear across multiple tables. Things like customer IDs, postal codes, or monetary amounts, and define them as UDTs. Get comfortable with the casting behavior. Build some UDFs that use UDTs in their signatures. Once developers see the benefits of schema clarity and type safety, they’ll naturally find more places to apply them.

At the end of the day, the best data platforms aren’t just fast and scalable. They’re understandable. UDTs are a step toward schemas that speak the language of the business, and that’s a step worth taking.

Need help or hands-on guidance? Ronny Steelman has over 20 years of experience in data development and architecture, with more than 8 years working with Snowflake, is a two-time published Snowflake author, and a 2026 Snowflake Data Superhero.