Overview
Text to Operations in eZintegrations enables users to transform, restructure, and clean data using either plain English instructions or custom Python code. It converts natural language inputs into executable Python logic or runs user-defined scripts directly.
This feature simplifies data transformation for emails, APIs, WebSocket streams, and database records by eliminating the need for complex scripting. Users can focus on describing outcomes instead of managing technical implementation.
When to Use
Use Text to Operations when data requires transformation before being sent to downstream systems.
- Modifying field values such as text casing
- Creating or removing keys in JSON objects
- Restructuring API responses
- Normalizing database records
- Applying custom business rules
- Automating data cleanup workflows
How It Works
Text to Operations processes incoming datasets using either natural language instructions or Python scripts. The platform translates plain English into executable Python code or runs the provided code directly.
The system automatically handles:
- pycode_data: The incoming source dataset
- Responsedata: The transformed output dataset
Users only need to define transformation logic. Input and output handling is managed automatically.
How to Configure / How to Use
Follow these steps to configure Text to Operations in an integration workflow.
- Open the Integration Bridge in eZintegrations.
- Add the Text to Operations component after the source and before the target.
- Enter a natural language instruction or paste Python code.
- Enable “Forward Code” if generated code must be reused.
- Preview the output for validation.
- Deploy the workflow.
The system automatically generates or executes Python code based on the provided input.
Core Feature: Simple Instructions, Big Results
Text to Operations converts user instructions into executable transformations. It enables rapid data processing without manual scripting.
Example 1: Converting a Field to Uppercase
Input data from an API:
{
"bizdata_dataset_response": {
"data": [
{
"id": 1,
"email": "george.bluth@reqres.in",
"first_name": "George",
"last_name": "Bluth",
"avatar": "https://reqres.in/img/faces/1-image.jpg"
}
]
}
}
Instruction:
From the source data, convert the last_name key to uppercase.
Generated Code:
Responsedata = []
for user in pycode_data['bizdata_dataset_response']['data']:
user['last_name'] = user['last_name'].upper()
Responsedata.append(user)
Output:
[
{
"id": 1,
"email": "george.bluth@reqres.in",
"first_name": "George",
"last_name": "BLUTH",
"avatar": "https://reqres.in/img/faces/1-image.jpg",
"python_code": "Responsedata = []..."
}
]
Example 2: Creating New Keys from Attributes
Input data:
[
{
"attributeid": 212077,
"attributename": "item_no",
"attributevalue": "999898",
"attribute_groupid": 24315,
"attribute_groupname": "Default",
"Isvariant": "false"
},
{
"attributeid": 212078,
"attributename": "Product Name",
"attributevalue": "Product ABC",
"attribute_groupid": 24315,
"attribute_groupname": "Default",
"Isvariant": "false"
}
]
Instruction:
For each item in the source, create a new key using the value of 'attributename', set its value to the value of 'attributevalue', and remove the original keys.
Generated Code:
Responsedata = []
for item in pycode_data:
new_item = item.copy()
new_key = new_item.pop('attributename', None)
new_value = new_item.pop('attributevalue', None)
if new_key and new_value:
new_item[new_key] = new_value
Responsedata.append(new_item)
Output:
[
{
"attributeid": 212077,
"item_no": "999898",
"attribute_groupid": 24315,
"attribute_groupname": "Default",
"Isvariant": "false"
},
{
"attributeid": 212078,
"Product Name": "Product ABC",
"attribute_groupid": 24315,
"attribute_groupname": "Default",
"Isvariant": "false"
}
]
Automation and Scheduling
Text to Operations workflows can run on automated schedules and scale for high-volume processing.
0 14 * * 1
This example schedules execution every Monday at 2:00 PM.
Troubleshooting
Use the following guidelines to resolve common Text to Operations issues.
- Incorrect Output: Review instruction clarity and field names.
- Script Errors: Validate Python syntax and data paths.
- Missing Fields: Confirm source data structure.
- Execution Failures: Check system logs and pipeline dependencies.
- Unexpected Data Types: Validate input formats.
Frequently Asked Questions
What is Text to Operations in eZintegrations?
Text to Operations is a feature that transforms data using natural language instructions or Python code.
Do I need programming skills to use it?
No. Users can write plain English instructions without coding knowledge.
Can I use custom Python scripts?
Yes. Users can paste and execute their own Python code.
How is output data generated?
Output is stored in the Responsedata variable and passed to downstream systems automatically.
Can this feature handle complex transformations?
Yes. It supports multi-step logic, restructuring, and custom data processing.
Notes
- Use precise field names in natural language instructions.
- Preview generated code before production deployment.
- Enable Forward Code when reuse is required.
- Monitor logs for execution accuracy.
Conclusion
Text to Operations enables fast, flexible, and reliable data transformation using either words or code. It reduces development effort, minimizes errors, and accelerates integration workflows.
By combining natural language processing with Python execution, eZintegrations provides a powerful solution for enterprise data automation.
