Snowflake offers a powerful way to store and process semi-structured data like JSON, allowing data analysts to derive valuable insight from the data using standard SQL. However, for data analysts to access the value in this data, data engineers must first extract the nested JSON data into relational tables to process it.
Managing a large amount of JSON data is a manual process that takes time away from data projects, introduces risks, and slows down Snowflake's use and adoption.
In addition, with more stringent data privacy regulations arising from the need to ensure every approval for data access and sharing requests adheres to governance policies, the process can be daunting and costly.
In this solution brief, we will explore how the combined solution provides the enterprise-class analytic engineering automation that allows your data team export data from nested JSON files into relational tables, simplifying the process and expediting access to your data.
Click here to learn more