You know how tricky it can get when you’re working with data from multiple sources — different formats, conditions, or exceptions that force you to duplicate logic or build messy workarounds? We’ve been there too.
That’s why we introduced Dynamic Conditions in Data Transformation.
Dynamic Conditions let you define rules that determine which input columns should be transformed, and when.
These conditions are fully customizable and based on the logic you configure — so your transformations can respond intelligently to variations in data.
For example, you can now:
All of this is done within one transformation node, making complex workflows easier to build and manage.
It’s all about giving you more control, more precision, and way less clutter when designing complex workflows. One node, many possibilities — smarter, not harder.
You know how tricky it can get when you’re working with data from multiple sources — different formats, conditions, or exceptions that force you to duplicate logic or build messy workarounds? We’ve been there too.
That’s why we introduced Dynamic Conditions in Data Transformation.
Dynamic Conditions let you define rules that determine which input columns should be transformed, and when.
These conditions are fully customizable and based on the logic you configure — so your transformations can respond intelligently to variations in data.
For example, you can now:
All of this is done within one transformation node, making complex workflows easier to build and manage.
It’s all about giving you more control, more precision, and way less clutter when designing complex workflows. One node, many possibilities — smarter, not harder.