Snowflake makes handling Semi-Structured Data simple. Handling both structured and semi-structured data in a modern cloud-based data warehousing can be difficult. One of Snowflake’s features is its Variant data type, which enables users to store and query JSON, XML, Avro, and other semi-structured data formats alongside structured data. This opens up new possibilities for data analysis and insights. Users can easily query and derive insights from all their data in a single location. In this blog post, we’ll explore Snowflake’s Variant data type in more detail. Further we will look how it gives Snowflake a unique advantage over its competitors in the cloud data warehousing space.

What is the Variant data type?

Snowflake’s Variant data type is flexible and dynamic which allows users to store semi-structured data without the need to clean them. Semi-structured data is data that does not conform to a rigid schema or structure, such as JSON or XML data. The Variant data type can be used to store these types of data in a column of a table in a Snowflake database and query this data.

How does it work?

The Variant data type which can be used in a Snowflake environment is a versatile and adaptive option for storing semi-structured data in diverse formats. When semi-structured data is uploaded to Snowflake, it can be automatically parsed and stored as a Variant data type. This feature allows Snowflake to manage complex data structures with nested and repeated fields efficiently. It also supports schema-on-read processing, which means that the data structure can be determined when queried instead of when loaded. Variant supports multiple data operations, such as filtering, aggregating, and querying. Additionally, Python and Java scripts can access the stored data, making it a robust tool for analyzing complex data. Furthermore, Snowflake’s Variant data type accommodates numerous data formats, including JSON, Avro, ORC, Parquet or XML.

Why is it important?

The ability to store and query semi-structured data is becoming increasingly important in today’s data-driven business landscape. Many modern applications generate or consume semi-structured data, such as mobile apps, web services or IoT devices. By supporting the Variant data type, Snowflake allows users to store and analyze this type of data alongside their structured data. In addition, the Variant data type in Snowflake enables users to take advantage of advanced querying capabilities. Snowflake supports querying of semi-structured data using a SQL-like syntax. For example, we can use the dot notation to extract specific values from a JSON object stored in a Variant column:

SELECT data:customer.name, data:customer.email
FROM example_table

This query would extract the “name” and “email” fields from a JSON object stored in the “data” column of the “example_table” table.

How does the Variant data type compare to other data warehousing solutions?

Although numerous data warehousing solutions incorporate semi-structured data to some extent, Snowflake’s use of the Variant data type provides several distinctive benefits. The efficient storage and retrieval of semi-structured data is one such advantage. Additionally, Snowflake’s ability to query semi-structured data using SQL-like syntax streamlines the process for users and removes the need for specialized programming knowledge. Below is an example of how the Variant data type can be used in Snowflake:

--Create table with a Variant column
CREATE TABLE nimbus_table (nim_id INTEGER, nim_data VARIANT);

--Insert a row of data into the table
INSERT INTO nimbus_table (nim_id, nim_data)
SELECT 1, PARSE_JSON(
'{"customer": {"name": "Lilya Nimbus", "email": "l.nimbus@email.com"},
"product": {"name": "CloudGenerator", "price": "56.50"}}');

--Select data about customer from variant column
SELECT nim_data:customer.name, nim_data:customer.email
FROM nimbus_table;

--Select data about product from variant column
SELECT nim_data:product.name, nim_data:product.price
FROM nimbus_table;

In this example, we create a table with an “nim_id” column and a “nim_data” column of type Variant. We then insert a row of data into the table, containing a JSON object with a “customer” object that has a “name” and “email” field as well as a “product” object with “name” and “price”. Finally, we query the data from the table using the dot notation to extract the “name” and “email” fields from the “customer” object as well for the “name” and “price” for the “product” object.

Practical Implications of Snowflake’s Variant Data Type for Businesses

But what does the support for semi-structured data in Snowflake’s Variant data type actually mean for businesses? The answer is: a lot! With the rise of big data, the ability to handle unstructured and semi-structured data has become increasingly important. A lot of valuable information is stored in formats like JSON, XML, and Avro, which are not easily stored or analyzed using traditional relational database management systems.

By allowing businesses to store and query semi-structured data alongside structured data in a single data warehouse, Snowflake makes it easier to derive insights and make informed decisions based on all available data.

Final thoughts

To sum up, Snowflake’s Variant data type sets it apart from other cloud-based data warehousing solutions by efficiently handling semi-structured data while remaining scalable and flexible. It enables the user to easily import, store, and query various data formats, such as JSON, Avro, or Parquet. This can be beneficial for applications that use a variety of data sources.

Make sure to check out the other Blogs of Nimbus Intelligence!