How do dynamic tables differ from traditional tables with manual data manipulation (DML)?

The key difference between dynamic tables and traditional tables with manual Data Manipulation Language (DML) lies in how the data is managed and updated:

Traditional Tables with DML:

• Data Manipulation: You directly manipulate the data within the table using DML statements like INSERT, UPDATE, and DELETE. This gives you full control over individual data points.
• Manual Updates: You need to write code or scripts to transform and update the data. This can be complex and time-consuming for intricate transformations.
• Separate Workflows: Data transformation is a separate process from data storage. You might need to schedule scripts or jobs to keep the transformed data updated.

Dynamic Tables:

• Immutable Data: The content of a dynamic table is based on a pre-defined SQL query. You cannot directly modify the data within the table itself.
• Automatic Updates: Dynamic tables refresh automatically based on a schedule or when the underlying data changes. Snowflake handles the transformation logic defined in the query.
• Declarative Approach: You define the desired transformed data through a SQL statement. Snowflake takes care of the entire transformation pipeline.

In essence:

• Traditional tables offer granular control over data but require manual effort for transformations.
• Dynamic tables simplify workflows by automating transformations and updates based on a defined query.