Summary

With the rising popularity of web applications and IoT data, semi-structured data has gained prominence for its flexibility in creating and loading dynamically changing objects without affecting ELT pipelines. Semi-structured formats, such as JSON, can handle any amount of variable nested data, which doesn’t need to conform to a pre-defined structure. Snowflake makes working with semi-structured formats easy thanks to its VARIANT data type – optimized for storage and analytical queries using easy-to-learn extensions to ANSI-standard SQL.

Querying a VARIANT data type provides the same performance as standard relational data types without needing to analyze the structure ahead of time – an approach known as schema-on-read. This means Snowflake users can work with semi-structured and relational data on the same platform using familiar SQL commands. However, although Snowflake gives users all the tools necessary for analyzing semi-structured data, schema...