Drag and Drop Transformations
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|Drag-and-drop Transformations feature is currently in BETA phase.|
Hevo provides the following drag-and-drop Transformation blocks by default. You can configure them for your use and test them before deploying:
Building a Transformation
Points to remember
Currently, you cannot combine both Python-based and drag-and-drop blocks in the same Transformation.
You must be in the BUILD tab to add or delete Transformation blocks or specify the filters and settings.
While building a Transformation, add filters in the order in which these will be applied to the data.
The IF-ELSE block branches your code to apply the conditional Transformation. Currently, you cannot merge the branches post-transformation.
You can drag and drop a Transformation block to the canvas and configure its settings in the right pane.
1. Data Filters
By default, the Transformation is applied to all Events and all Fields. However, you can filter the fields to be finally updated on the basis of the following:
Event Type: The Transformation is applied to the selected Event Type. For example, Event type of name,
Event Field: The Transformation is applied to all Events that have this Event Field or Event Field Value. You can filter Events based on the properties of the Event. For example, you can select all Events that contain a field called
price. This means the field-level filters, if any, and the Transformation, will be applied to all Events that have the field,
price. This does not mean that the intent is to apply the Transformation only to the field
Note: Events not matching the filters in a Transformation block remain unchanged.
Field Name: The Transformation is applied to the specified fields. For example, in the Event Type
dealscontaining a field by the name
price, if a field with the name
archivedis present, the Transformation must be applied on it.
Field Value: The Transformation is applied to all fields having this value. For example, if the value of the
true, the Transformation must be applied to it.
Then, the final condition is derived as: For Event of Type
deals and having the field
price, if the value of the
archived field is
true, then apply the Transformation as specified in the SETTINGS.
Note: In case the field to which the Transformation is applied is of the type Array or List, the Transformation is applied to each element of the list instead of the list itself. Exception to this are: “Create separate fields while splitting” and “Flatten JSON”, where the Transformation cannot be applied to the list.
You can select the appropriate operator and values for each of these filters.
2. Transformation settings
Settings define the action to be taken. For example, Field Name and New Field Name define which field name has to be changed and what its new value should be. The SETTINGS are listed below the filters.
3. Transformation workflows
You can apply multiple Transformations to the data by creating a workflow using the required Transformation blocks. If each Transformation is independent of the others, the sequence in which you connect the blocks is immaterial, and each Transformation can call a different Event Type. However, if each block builds upon the previous, then the sequence of connecting the blocks is important. A sequential workflow can be designed on one Event Type only.
The Events not matching the filters remain unchanged. In addition, all Events are passed on to the next Transformation block.
Note: You can drag and drop a block into an existing workflow sequence.
4. Incorrect settings
In case settings are missing or incorrect, an indicator is displayed next to the block. Hover the mouse over it to view the error text.
Refer to the following table for the list of key updates made to this page:
|Date||Release||Description of Change|
|Jul-12-2022||NA||- Added sections, Points to remember and Getting started.
- Removed section, Features and Limitations, and merged it in Building a Transformation.