What Data Structure Types Can Be Inserted Into a Variant Column in a Snowflake Table?


Angela Bailey

When working with Snowflake tables, you may come across a scenario where you need to store different types of data in a single column. Snowflake provides a variant data type specifically designed for this purpose. The variant data type allows you to store semi-structured and nested data within a single column, making it incredibly flexible for various use cases.

What is the Variant Data Type?

The variant data type in Snowflake is similar to the JSON (JavaScript Object Notation) format. It can store complex and hierarchical structures such as arrays and objects, making it suitable for storing diverse data types. Whether you have integers, strings, booleans, or even entire JSON objects, you can insert them into a variant column.

Supported Data Structure Types

The variant data type supports several different data structure types that can be inserted into a Snowflake table’s variant column:

  • Primitive Data Types: These include basic types such as integers, floats, strings, booleans, and null values.
  • Arrays: You can insert arrays of any supported data type into a variant column. For example, an array of integers or an array of strings.
  • Objects: Snowflake’s variant data type allows you to insert JSON-like objects into a single column.

    Objects can have key-value pairs where values can be any supported data type.

  • Nested Structures: You can create complex structures by nesting arrays and objects within each other. For example, an object containing an array of objects or an array containing objects with arrays inside.


The flexibility of the variant data type comes with the responsibility of ensuring data integrity and consistency. Although you have the freedom to store different data types in a variant column, it is essential to maintain a consistent structure within the column to ensure efficient querying and processing.

Querying Variant Columns

When querying a Snowflake table with a variant column, you can access the data using SQL syntax that works with JSON-like structures. Snowflake provides several functions and operators specifically designed for working with variant columns. These include GET, FLATTEN, and ARRAY_AGG, among others.

By utilizing these functions, you can easily navigate through the nested structures within a variant column, extract specific values, filter based on certain conditions, and even aggregate data.

Querying variant columns effectively requires a good understanding of SQL and familiarity with Snowflake’s functions and operators for working with semi-structured data.


The variant data type in Snowflake provides immense flexibility when it comes to storing diverse types of data within a single column. Whether you need to store primitive types, arrays, objects, or nested structures, the variant type has got you covered.

However, it is crucial to maintain consistency and integrity within your variant columns to ensure efficient querying and processing. Take advantage of Snowflake’s built-in functions for working with semi-structured data to unlock the full power of your variant columns.

So go ahead and leverage the power of variants in Snowflake to handle complex and diverse data types effortlessly!

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