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Businesses often experience changes that necessitate updates to data schemas, such as adding new columns to datasets. This process is now streamlined, allowing users to modify data sources without disrupting existing data ingestion. Previously, users had to create a duplicate data source to add new columns, which was inefficient for large datasets. The updated method, accessible via CLI, REST API, or UI, enables straightforward schema modifications by appending new columns at the end, with default values automatically assigned to ensure continuity in data imports. Users must consider certain caveats, such as the potential disruption of materialized views using SELECT * queries and limitations to data sources with specific engine types. Default values are used for new columns if no data is provided, ensuring that existing workflows continue smoothly until new data with the updated schema is ingested.