Star and snowflake schemas are two essential dimensional modeling techniques used in data warehousing, each with its own advantages and disadvantages for organizing data for analytics. Star schemas prioritize read performance with denormalized tables, while snowflake schemas introduce normalization to reduce redundancy and improve data integrity, albeit at the cost of query complexity and performance. Understanding these differences is crucial for data and analytics engineers when designing effective data models in modern tools like dbt.