Dynamic Columns

A dynamic column is a special type of column in a dataset that does not store fixed values but instead generates its data dynamically using predefined logic. Unlike static columns, which contain manually entered or fixed values, dynamic columns compute their values based on automated processes such as custom Python code execution, API calls, conditional logic, and data transformations.

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Key Characteristics of Dynamic Columns

• Automated Value Generation: Values are computed dynamically rather than manually entered.
• Regenerable Data: Dynamic columns can refresh their values when the underlying logic or data changes.

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How Dynamic Columns Work

1. User selects a dynamic column type and configures its settings.
2. The system uses parallel execution to compute values efficiently for large datasets.
3. Each row in the column receives a computed value based on the logic defined.

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Why Use Dynamic Columns?

• Efficiency: Reduces manual data entry and updates values automatically.
• Scalability: Works efficiently on large datasets with thousands of rows.
• Flexibility: Supports various logic types, from simple conditions to external data integrations.
• Data Consistency: Ensures uniform value generation across the dataset.

By leveraging dynamic columns, users can automate data transformation, fetch external insights, and apply complex logic, making their datasets more powerful and adaptive

Click here to learn how to create dynamic columns