Dynamic tables performance¶
Dynamic tables are intentionally designed to be simple: easy to create, use, and manage. They simplify data engineering by minimizing configuration complexity and Snowflake continuously enhances their performance characteristics to benefit users without requiring additional effort.
Performance engineering involves understanding the system, experimenting with ideas, and iterating based on what works. The following topics outline steps to optimize your dynamic tables’ performance, which can help save costs, reduce data lag, and improve response times. These topics also provide guidance on maximizing the use of incremental refresh performance on dynamic tables.
Before you begin, ensure that you have a clear understanding of your requirements for cost, data lag, and response times.
Note
When queried, dynamic tables perform similarly to regular Snowflake tables. For more information, see Optimizing performance in Snowflake.