What is a best practice for integrating semi-structured data with BI tools?

Creating a Snowflake view that parses semi-structured columns is a best practice for integrating semi-structured data with BI tools because it allows for flexible querying of the data while maintaining its original structure. Semi-structured data, such as JSON or Avro formats, might not conform to a rigid schema, and a view enables users to transform the data as necessary for their analysis purposes. By defining the parsing logic in the view, users can query the semi-structured data using standard SQL syntax, making it easier to integrate and analyze within their BI tools. This approach ensures that the data remains accessible and usable, even when its structure varies or changes over time.

In contrast, loading semi-structured data directly into a structured table may lead to issues with data integrity and usability if the data does not fit the predefined schema. Creating a metadata object in a BI tool can be useful, but it doesn’t directly address the challenge of querying semi-structured data effectively. Hence, while different approaches might be valuable in specific contexts, the creation of views specifically tailored for parsing is a more robust and flexible best practice.