How do BigQuery and Snowflake handle schema evolution in JSON files?

BigQuery and Snowflake handle JSON schema evolution differently, each with tradeoffs. BigQuery automatically adapts to new fields in JSON when using schema auto-detection or flexible schema definitions. New fields appear immediately without table modifications. BigQuery supports adding nullable columns easily. However, changing field types requires recreation. Schema evolution with JSON columns requires no migrations—new fields just work. Snowflake VARIANT type handles any schema changes automatically with no modifications needed. New nested fields, removed fields, and type changes work seamlessly. Snowflake semi-structured data is fully schema-on-read. However, query performance degrades without schema optimization. Both platforms support schema evolution better than traditional databases. BigQuery excels with predictable evolution and optimization. Snowflake offers more flexibility for chaotic schemas. For production systems, document schema expectations even with flexible types. Test schema changes with our JSON Validator at jsonconsole.com/json-editor before deploying. Both platforms enable agile development with evolving data structures. Choose based on whether you prioritize performance (BigQuery) or ultimate flexibility (Snowflake). Hybrid flattened plus JSON approach works well in both.