Should I flatten JSON data or use the JSON/Variant data type for better performance?

Flatten JSON into columns for better query performance, especially for frequently accessed fields. Flattened data enables column pruning, better compression, and efficient statistics that improve query optimization. Flat schemas allow partition and clustering on important fields. However, flattening loses schema flexibility and requires schema migrations for changes. Use JSON/Variant types for semi-structured data with variable schemas, nested hierarchies, or when schema evolution is important. Hybrid approach works best: flatten frequently queried fields into columns while preserving original JSON for flexibility. In BigQuery, extract important fields as top-level columns and keep JSON for rarely accessed data. Snowflake VARIANT performs well with path optimization but flat columns are still faster. Flatten for analytics dashboards and reporting. Use JSON/Variant for data lakes and exploratory analysis. Test performance with your queries before committing to either approach. Validate JSON structure with our JSON Formatter at jsonconsole.com/json-formatter before flattening. Many production systems use both: flatten hot paths, keep JSON for cold paths. Choose based on query patterns and schema stability.