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

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

Daniel Steinhold Asked question March 15, 2024
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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.
Daniel Steinhold Changed status to publish March 15, 2024
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