Which Data Type in Snowflake Allows Storage of Semi Structured Data?


Heather Bennett

In Snowflake, the data type that allows storage of semi-structured data is the VARIANT data type. VARIANT is a powerful and flexible data type that enables you to store JSON, XML, Avro, or other semi-structured data within your tables.

What is Semi-Structured Data?

Semi-structured data refers to data that does not conform to a specific schema or structure like traditional structured data. It contains elements with varying structures and can include nested objects, arrays, and key-value pairs.

The VARIANT Data Type

The VARIANT data type in Snowflake allows you to store semi-structured data in a column of a table. It can hold values of different types such as JSON objects, arrays, strings, numbers, booleans, and nulls.

When creating a table in Snowflake that needs to store semi-structured data, you can define a column with the VARIANT data type:

CREATE TABLE my_table (
  id INT,
  payload VARIANT

The above example creates a table named my_table with two columns: id, which is of type INT, and payload, which is of type VARIANT.

Incorporating Semi-Structured Data with VARIANT Data Type

The flexibility of the VARIANT data type allows you to easily incorporate semi-structured data into your Snowflake tables. You can use it for various use cases:

  • JSON Data: Store and query JSON documents directly within your tables using the VARIANT data type. This enables you to leverage Snowflake’s powerful querying capabilities on semi-structured data.
  • XML Data: Similarly, you can store and query XML documents using the VARIANT data type.

    Snowflake supports XML parsing functions that allow you to extract data from XML documents stored in VARIANT columns.

  • Avro Data: Avro is a popular binary serialization format used for efficient storage and processing of data. With the VARIANT data type, you can store Avro-encoded data within your tables and utilize Snowflake’s native support for Avro.

Querying Semi-Structured Data

Snowflake provides powerful functions and operators to query semi-structured data stored in VARIANT columns. You can use dot notation to navigate through JSON or XML structures, access specific fields, and perform complex transformations.

For example, let’s say we have a table named my_table, which contains a column named payload, storing JSON documents. Here’s how you can query specific fields within the JSON:

SELECT payload:user.name AS user_name,
       payload:age AS user_age
FROM my_table;

The above query uses dot notation (:) to access the name field within the nested user object and the top-level age field from each JSON document stored in the VARIANT column.


The VARIANT data type is not recommended for storing large amounts of semi-structured data. If you have very large JSON or XML documents, it’s advisable to store them in a separate table and use a VARIANT column to reference the document’s identifier or location.


The VARIANT data type in Snowflake allows you to store and query semi-structured data, such as JSON, XML, or Avro-encoded data, within your tables. It provides the flexibility required to work with diverse data formats while leveraging Snowflake’s powerful querying capabilities.

By incorporating semi-structured data using the VARIANT data type, you can unlock new insights from your data and enhance your analytical capabilities.

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