Snowflake data types: A comprehensive reference guide

Snowflake has become a leading choice for cloud data warehousing thanks to its flexibility, performance, and ease of use. But to get the most out of the platform, it’s important to understand how it handles data at a foundational level, starting with data types.

Data types define how information is stored, processed, and interpreted inside Snowflake. Choosing the right types can improve performance, support better analytics, and ensure long-term scalability across your data models.

In this guide, we’ll explore the range of data types Snowflake supports, highlight when and why each is used, and provide practical guidance for building well-structured, efficient tables in your warehouse.

Main takeaways from this article:

• Understanding Snowflake data types is essential for designing efficient, scalable tables and ensuring accurate analysis across structured and semi-structured data.
• Snowflake supports a wide variety of data types—including numeric, string, logical, date/time, semi-structured (like JSON), and geospatial—to meet diverse analytical needs.
• Snowflake does not support some types like ROW, MULTISET, PERIOD, and native XML, so transformations are required to maintain compatibility.
• RudderStack helps teams deliver clean, typed data directly to Snowflake, ensuring schema alignment, reducing friction, and enabling smarter analytics at scale.

What is Snowflake?

Snowflake is a cloud-native data platform designed for scalable, high-performance data storage, processing, and analytics. Unlike traditional on-premises warehouses, Snowflake separates storage and compute, allowing teams to scale each independently based on workload and cost efficiency.

Its performance and flexibility have led to rapid enterprise adoption. As of Q1 fiscal 2026, Snowflake reported $1 billion in total revenue, a 26% year-over-year increase, with product revenue reaching $996.8 million. This growth underscores the platform’s role in powering modern, AI-ready data infrastructure for thousands of global organizations.

Snowflake supports structured and semi-structured data, making it easy to centralize everything from transactional records to event logs and JSON payloads. Its architecture is built to handle high concurrency, so multiple users and systems can query data simultaneously without performance issues.

Security and governance are built in, with features like end-to-end encryption, role-based access control, and support for industry compliance standards. And with support for SQL, JavaScript, and Snowpark languages like Python and Scala, Snowflake integrates easily with modern data stacks—whether you're building real-time dashboards, training machine learning models, or activating customer data.

The six data types supported by Snowflake

Here’s a quick reference to the main categories of Snowflake data types and when to use them:

Now let's dive into the different data types supported in more detail.

Numeric data types

Numeric types are essential for storing values you plan to calculate, sum, or compare, like totals, percentages, and measurements.

➤ NUMBER(p, s) / DECIMAL(p, s) / NUMERIC(p, s)

These are precision-safe data types used to store fixed-point numbers. They’re all interchangeable in Snowflake.

• Precision (p): Total number of digits (max 38)
• Scale (s): Digits to the right of the decimal

Best for: Currency, interest rates, financial metrics, quantities requiring exact arithmetic

Example: amount DECIMAL(10,2) -- up to 10 digits, with 2 after the decimal

➤ INTEGER / INT / BIGINT / SMALLINT / BYTEINT

All are aliases for NUMBER(38,0)—though using different aliases helps signal intent.

• SMALLINT: Up to 5-digit integers
• BIGINT: Used when expecting very large numbers (e.g., row counts, bytes)

Example:

CREATE TABLE devices (

device_id BIGINT,

status_code SMALLINT

);

➤ FLOAT, FLOAT4, FLOAT8, REAL, DOUBLE

These are approximate-number data types based on IEEE 754 floating-point arithmetic.

• FLOAT: Short for FLOAT8, a 64-bit representation
• FLOAT4: Lower-precision alternative (rarely used directly)
• REAL / DOUBLE: Aliases for FLOAT

Use when:

• Precision isn’t critical
• You’re storing scientific measurements or probabilities

Example: sensor_accuracy FLOAT8

Special considerations

• All numeric types default to NUMBER(38,0) unless specified
• Floating point values can store NaN, inf, and -inf, which can affect query logic
• Use integer types for keys, counts, or category IDs; use decimal types for money or percentages

String and binary data types

These types handle textual and binary content, including user-generated data, file storage, and encoded metadata.

➤ VARCHAR(n)

Stores strings up to 16 MB in length. The n is optional and only used for documentation—Snowflake does not enforce it.

Best for: Usernames, comments, serialized data, product SKUs

Example: email VARCHAR(255)

➤ CHAR(n) / CHARACTER(n)

Fixed-length strings. Snowflake stores them without right-padding with spaces (unlike some RDBMS).

Example: state_code CHAR(2)

➤ TEXT, STRING

Synonyms for VARCHAR. Often used for compatibility with external tools (e.g., BI tools, ETL platforms).

➤ BINARY(n) / VARBINARY(n)

Stores raw binary data in bytes. VARBINARY allows flexible-length content.

Use case: File uploads, image storage, encrypted values, checksums

Example:

CREATE TABLE documents (

doc_id INT,

content BINARY

);

Special considerations

• All string values use UTF-8 encoding
• Unicode characters may consume multiple bytes
• Use BINARY for non-text content; VARCHAR for strings
• Snowflake does not right-pad CHAR values—treat them like VARCHAR

Logical data types

These are used to store boolean (true/false/unknown) values.

➤ BOOLEAN

Supports TRUE, FALSE, and NULL.

Use case: Status flags, feature toggles, yes/no logic in analytics

Example: is_subscribed BOOLEAN

Usage tips:

• Use CASE for logic-based column transformations
• Filter with expressions like WHERE is_active = TRUE

Special considerations

• No arithmetic allowed on BOOLEAN types
• Explicit casting may be needed when converting from strings or numbers
• Use TRY_CAST() to avoid errors during conversion

Date and time data types

Used to track durations, events, and time-based metrics—critical for analysis, logging, and compliance.

➤ DATE

Calendar date, no time. Stored as YYYY-MM-DD.

Use case: Registration dates, order dates, report cutoffs

Example: created_date DATE

➤ TIME

Captures only the time of day (HH:MM:SS), without date. Supports fractional seconds.

Use case: Daily scheduling, reservations, clock-in/clock-out data

Example: start_time TIME(3)

➤ TIMESTAMP_NTZ (no time zone)

Neutral timestamp stored as UTC, displayed as-is.

Use case: Internal logs or consistent time storage

➤ TIMESTAMP_LTZ (local time zone)

Adjusted to the user’s session time zone when queried.

Use case: Regional views, frontend display of user-local time

➤ TIMESTAMP_TZ (with time zone)

Stores explicit offset (e.g., +05:00). Recommended for cross-regional reporting.

Example: event_at TIMESTAMP_TZ

Special considerations

• Set session time zone using ALTER SESSION SET TIMEZONE = 'America/New_York'
• Snowflake stores all timestamps in UTC behind the scenes
• TIMESTAMP_TZ only captures offsets, not named zones (DST changes not tracked)

Semi-structured data types

These allow Snowflake to ingest and query JSON-like structures without flattening on ingestion.

➤ VARIANT

Snowflake’s universal semi-structured data type. Stores JSON, XML (via conversion), Parquet, Avro, etc.

Best for: API payloads, event logs, external system data

Example:

CREATE TABLE logs (

log_id INT,

raw_event VARIANT

);

Querying example:

SELECT raw_event:browser, raw_event['user_id'] FROM logs;

➤ OBJECT

Key-value pairs (strings only for keys). Similar to JSON objects.

Use case: Dynamic attributes, settings, configurations

➤ ARRAY

Ordered list of values.

Use case: Tags, multi-value fields, click paths

Example: SELECT ARRAY_SIZE(raw_event:tags)

Special considerations

• Use PARSE_JSON() to ingest external JSON
• Avoid highly nested structures when possible—they increase processing time
• Semi-structured fields are not as performant as structured ones—use for flexibility, not performance

Geospatial data types

Used for storing coordinates and geometric shapes for mapping, proximity, and routing analysis.

➤ GEOGRAPHY

Represents global coordinates using latitude and longitude (WGS 84).

Use case: Store locations, boundaries, service areas

Example:

CREATE TABLE locations (

place_id INT,

coordinates GEOGRAPHY

);

INSERT INTO locations VALUES (1, 'POINT(-122.35 37.55)');

Functions: ST_DISTANCE(), ST_WITHIN(), ST_INTERSECTS()

➤ GEOMETRY

Uses a flat Cartesian coordinate system. Best for non-Earth applications (e.g., floor plans).

Example:

CREATE TABLE warehouse_layout (

sensor_id INT,

zone GEOMETRY

);

Special considerations

• Only 2D coordinates are supported (no altitude)
• Direct casting between GEOGRAPHY and GEOMETRY isn’t allowed—use GeoJSON to bridge
• Geospatial queries are more compute-intensive—test performance early

Unsupported data types in Snowflake

While Snowflake supports a broad range of data types, there are a few that it does not currently support. Being aware of these can help you avoid issues when loading or transforming data.

• ROW: Not supported because Snowflake doesn't allow nested row structures. Convert these to flat tables with individual columns.
• MULTISET: Unsupported due to lack of built-in support for duplicate-containing sets. Consider using arrays or normalized tables instead.
• PERIOD: Snowflake doesn't natively represent time ranges. Split these into separate start and end timestamp fields.
• XML: Snowflake doesn’t support XML as a native type. Convert XML data to JSON or extract key fields into structured columns.

Snowflake continues to expand its functionality, but for now, transforming these unsupported types into compatible formats is key to maintaining smooth data workflows. Always consult the official documentation for the latest data type support.

Snowflake data type conversion

In Snowflake, data type conversion—also known as casting—is a powerful feature that allows you to change the data type of a value, column, or expression. This is essential when transforming data during ETL, cleaning inconsistent schemas, or preparing fields for joins, filters, or analytics.

Snowflake supports two main forms of casting: explicit and implicit.

1. Explicit casting

Explicit casting is when you manually specify the data type to convert to. This gives you full control and ensures predictable behavior.

Syntax options:

SQL
Copy
-- CAST function

SELECT CAST('2024-01-01' AS DATE);

-- Double-colon syntax

SELECT '123'::NUMBER;

-- Built-in conversion functions

SELECT TO_DATE('01-02-2024', 'DD-MM-YYYY');

SELECT TO_BOOLEAN('true');

Use explicit casting when:

• You're loading data with inconsistent types (e.g., stringified numbers)
• You want to control formatting and avoid automatic assumptions
• You're preparing data for a strict schema

2. Implicit casting

Snowflake will automatically convert between compatible types in some cases, such as when combining a string and a number in an expression.

Examples:

SELECT 10 + '5'; -- returns 15 (VARCHAR → NUMBER)

SELECT 'User ID: ' || 12345; -- returns 'User ID: 12345'

Common conversion use cases

Conversion limitations and edge cases

• 🚫 Precision loss: Converting FLOAT to INTEGER will round or truncate.
• 🚫 Truncation errors: Converting large VARCHAR to smaller length may cause data loss.
• 🚫 Unsupported conversions: You can't cast OBJECT directly to BOOLEAN or DATE.
• 🚫 Null handling: Casting NULL preserves its null status, but some functions may return defaults or errors.

Best practices for casting

• ✅ Always validate string formats before casting to DATE, NUMBER, or BOOLEAN.
• ✅ Use TRY_CAST() if you're unsure whether the value will succeed—this returns NULL instead of failing:

SELECT TRY_CAST('abc' AS NUMBER); -- returns NULL

• ✅ Avoid excessive implicit casting in production pipelines—use explicit types for clarity.

Snowflake programming languages

Beyond its flexible data types, Snowflake supports multiple programming languages that help teams work with typed data in powerful ways:

• SQL – Core to querying, transforming, and casting data types within Snowflake.
• ANSI SQL – Ensures compatibility across systems and simplifies migration.
• JavaScript – Enables creation of user-defined functions (UDFs) and procedures that handle typed logic, validations, and conversions.
• Snowpark – Allows engineers and data scientists to write type-aware data transformations in Python, Java, or Scala—ideal for feature engineering, model preparation, and advanced workflows.

These language options make Snowflake more than just a storage layer. They enable flexible, type-safe data manipulation across analytics, ML, and application use cases—all while keeping compute close to the data.

Power smarter pipelines with RudderStack

RudderStack helps teams deliver real-time, clean, and structured data to Snowflake—automatically typed, schema-aligned, and ready for analysis. From event data to enriched customer traits, our platform ensures your warehouse reflects the dimensional models and data types that power meaningful insights.

Ready to unify and activate your data in Snowflake? Schedule a demo to see how RudderStack fits into your modern data stack.