If you're a developer, you've been there. You receive a blob of JSON from a third-party API or a legacy database. It's a chaotic maze of nested objects, inconsistent key names, and arrays buried three levels deep. Your mission: wrangle this data into a clean, flat structure that your application can actually use.
Traditionally, this means diving into a custom script. You'll spend hours writing brittle logic, chaining together object accessors, handling null checks, and looping through arrays. It's tedious, error-prone, and a maintenance nightmare. A small change in the source data can break your entire pipeline.
But what if you could just describe the final data structure you want, and have it generated for you? This is the promise of AI-powered data transformation, and it's changing how developers approach the common challenge of JSON restructuring.
Let's consider a realistic scenario. We have a nested JSON object representing customer data.
Input Data:
{
"customerRecord": {
"id": "cust_1a2b3c",
"personalInfo": {
"given_name": "John",
"family_name": "Appleseed",
"contact_methods": [
{"type": "email", "value": "john.a@web.com"},
{"type": "phone", "value": "555-0101"}
]
},
"purchase_history": [
{"item_id": "prod_x", "price": 19.99, "quantity": 2, "date": "2023-11-01"},
{"item_id": "prod_y", "price": 45.50, "quantity": 1, "date": "2023-11-15"}
],
"metadata": {"source": "legacy-db", "version": 1.2}
}
}
To transform this into a simple, flat object for a user profile page, you might write a Python or JavaScript function. This involves manually navigating paths, checking for existence, performing calculations, and building a new object piece by piece. The code is often verbose and tightly coupled to the input structure. If personalInfo or purchase_history is missing, your script fails.
This approach is not scalable, especially in modern ETL pipelines where data sources and schemas evolve.
Enter transform.do, an API-first platform designed to simplify complex data manipulation. Instead of writing imperative code (telling the computer how to do something), you provide simple, declarative instructions. You tell an AI agent what you want the final result to be.
Our Data as Code philosophy means your transformation logic is readable, maintainable, and incredibly powerful.
Let's tackle the same transformation using the transform.do agent. All it takes is the raw data and a set of plain-English instructions.
import { Agent } from '@do-sdk/agent';
const transformAgent = new Agent('transform.do');
const rawData = {
// The complex nested JSON from above
};
const transformedData = await transformAgent.run({
input: rawData,
instructions: `
1. Flatten the structure.
2. Rename 'customerRecord.id' to 'customerId'.
3. Combine 'personalInfo.given_name' and 'personalInfo.family_name' into a 'customerName' field.
4. Extract the email address into a top-level 'email' field.
5. Calculate the 'totalSpent' by summing the price * quantity for all items in 'purchase_history'.
6. Create a 'recentPurchaseDate' field with the latest date from 'purchase_history'.
7. Omit the original 'purchase_history' and 'metadata' fields.
`
});
// The AI agent returns the exact structure you requested:
console.log(transformedData);
Resulting Output:
{
"customerId": "cust_1a2b3c",
"customerName": "John Appleseed",
"email": "john.a@web.com",
"totalSpent": 85.48,
"recentPurchaseDate": "2023-11-15"
}
The difference is night and day. The logic is self-documenting, easy to modify, and far more resilient to changes in the source data. There's no complex code to maintain—just a simple API call.
While this example focuses on a single JSON object, the applications for scalable data pipelines are enormous. transform.do is a perfect fit for the 'T' (Transform) in modern ETL and ELT processes.
By leveraging an AI agent, you decouple your transformation logic from your core application code, creating more robust and maintainable data systems.
Q: What kind of data transformations can transform.do handle?
A: Our AI agents can perform a wide range of transformations, including format conversion (e.g., CSV to JSON), data cleaning (e.g., removing duplicates, standardizing values), restructuring (e.g., nesting objects, renaming keys), and data enrichment by combining or deriving new fields.
Q: How do I specify the transformation logic?
A: You provide the transformation logic through simple, natural language instructions in your API call. For more complex or repeatable tasks, you can define a 'Service-as-Software' workflow that encodes your exact business logic for consistent results.
Q: Is transform.do suitable for large-scale ETL pipelines?
A: Yes. transform.do can be a powerful component in modern ETL/ELT pipelines. It excels at the 'T' (Transform) step, allowing you to build flexible and intelligent data processing workflows that can be triggered via API calls, replacing brittle custom scripts.
Q: What data formats does transform.do support?
A: While JSON is native to the platform, our agents can be configured to process various formats like CSV, XML, and plain text. The platform is designed to be flexible, allowing you to define agents that handle your specific data input and output requirements.
Your time as a developer is too valuable to be spent on tedious data janitor work. By embracing an AI-driven approach to data manipulation and JSON transformation, you can build faster, ship more reliable features, and focus on the problems that matter.
Ready to simplify your data workflows? Explore transform.do and let an AI agent handle the heavy lifting.