Mastering Data Types in Snowflake - From Scalars to Semi-Structured

In this article, let’s discuss a range of diverse data structures for both structured and semi-structured data.

In today’s data-driven landscape, managing diverse data structures efficiently is paramount. Snowflake, a cloud-based data platform, offers a rich set of data types to handle both structured and semi-structured data seamlessly. This article delves into the various data types supported by Snowflake, focusing on their characteristics and practical applications.

Introduction to Snowflake Data Types#

Snowflake is more than just a cloud data warehouse—it’s a platform built for modern data workloads. One of its core strengths lies in its extensive support for different data types, allowing you to store, transform, and analyse everything from simple numbers to nested JSON documents.

Snowflake’s data types fall into three main categories:

• Scalar Types – Basic data such as strings and numbers.
• Semi-Structured Types – Designed for flexible, schema-less data like JSON or XML.
• Structured Types – Arrays, maps, and objects with type enforcement and schema definition.

Let’s explore each of these in more detail.

Scalar Data Types#

Scalar types are the foundation of most tables in Snowflake. These represent single, atomic values and include:

• Numeric: NUMBER, DECIMAL, INT, FLOAT – suitable for financial data, identifiers, and computations.
• String: VARCHAR, CHAR, TEXT – commonly used for names, codes, and any text-based information.
• Date/Time: DATE, TIME, TIMESTAMP_NTZ, TIMESTAMP_TZ, TIMESTAMP_LTZ – for storing temporal data with or without time zones.
• Boolean: BOOLEAN – stores TRUE, FALSE, or NULL values.
• Binary: BINARY – used for storing raw data such as images or file blobs.

These are straightforward and suitable for most traditional, structured datasets.

Note: Features change from time to time with new capabilities being added regularly. Review the documentation ↗ for the latest information.

Semi-Structured Data Types#

Snowflake excels when it comes to working with semi-structured data. This includes data that doesn’t fit neatly into rows and columns—think JSON documents, event logs, or API responses. Key types include:

VARIANT#

The VARIANT type can hold semi-structured data, including JSON, Avro, Parquet, ORC, and XML. It’s a flexible, catch-all type ideal for data with unpredictable or dynamic structures.

Example: Storing customer interaction logs or API responses.

OBJECT#

An OBJECT in Snowflake is similar to a JSON object—essentially a collection of key-value pairs. Keys are strings, and values can be any supported data type. This is useful when data has a structure but varies across records.

Example: A user profile object containing name, email, preferences, and optional social links.

ARRAY#

The ARRAY type stores ordered collections of elements. Arrays can contain any data type, including other arrays and objects, making them incredibly useful for hierarchical data.

Example: A list of items in a shopping basket or an array of time-series readings from a sensor.

Structured Types: ARRAY, OBJECT, and MAP#

Beyond semi-structured flexibility, Snowflake also supports structured types, which enforce schema and type consistency. These are distinct from semi-structured types and offer stronger guarantees for data quality.

⚠️ Limitations: Structured types are generally available but are not yet supported in all contexts (e.g., dynamic tables, external tables, and some hybrid table types). Always check current feature support in the structured types documentation ↗.

Structured ARRAY#

A structured array enforces that all elements are of the same defined type (e.g., integers, strings).

Use Case: An array of integers representing monthly sales figures.

Structured OBJECT#

Structured objects define specific keys and enforce types for their values. This bridges the gap between free-form JSON and fully structured tables.

Use Case: An address object with street, postcode, and country fields, all of which must be strings.

Features change from time to time with new features being added regularly, it is recommended that you review the documentation ↗ for the latest on what specific features are included with any of the Editions.

Practical Use Cases#

Snowflake’s data types enable a wide range of applications:

• Data Lakes: Store and query raw JSON files directly without ETL.

• Analytics: Use ARRAY and VARIANT to work with event streams or time-series data.

• IoT: Handle device-generated data with nested and inconsistent structures.

• E-commerce: Use structured OBJECT types to manage flexible product catalogues.

• Marketing: Analyse unstructured campaign metrics and customer behaviour logs.

Conclusion#

Snowflake’s diverse data types are central to its versatility as a modern data platform. From classic scalars to powerful semi-structured formats like VARIANT, ARRAY, and OBJECT, these types empower data engineers and analysts to model and process data intuitively and efficiently.

By understanding how and when to use each type, your team can design robust, scalable, and future-proof data pipelines—ready to support analytics, machine learning, and beyond.

Structured Collections: ARRAY, OBJECT and MAP#

Snowflake also supports structured versions of its collections, allowing you to define the data type of each element more precisely.

Structured ARRAY#

You can define an array with a consistent element type—for example, an array of numbers or strings. This helps ensure data quality and simplifies querying.

Use Case: A structured array of integers representing monthly sales figures.

Structured OBJECT#

Structured objects define specific keys and enforce types for their values. This bridges the gap between free-form JSON and fully structured tables.

Use Case: An address object with street, postcode, and country fields, all of which must be strings.

Features change from time to time with new features being added regularly, it is recommended that you review the documentation ↗ for the latest on what specific features are included with any of the Editions.

Practical Use Cases#

Snowflake’s data types enable a wide range of applications:

• Data Lakes: Store and query raw JSON files directly without ETL.

• Analytics: Use ARRAY and VARIANT to work with event streams or time-series data.

• IoT: Handle device-generated data with nested and inconsistent structures.

• E-commerce: Use structured OBJECT types to manage flexible product catalogues.

• Marketing: Analyse unstructured campaign metrics and customer behaviour logs.

Conclusion#

Snowflake’s diverse data types are central to its versatility as a modern data platform. From classic scalars to powerful semi-structured formats like VARIANT, ARRAY, and OBJECT, these types empower data engineers and analysts to model and process data intuitively and efficiently.

By understanding how and when to use each type, your team can design robust, scalable, and future-proof data pipelines—ready to support analytics, machine learning, and beyond.