Wrangling data is a universal developer challenge. You need to move information from service A to system B, but the formats are different. One uses snake_case keys and epoch timestamps, while the other expects camelCase and ISO 8601 dates. So begins the tedious cycle of writing, testing, and maintaining brittle ETL (Extract, Transform, Load) scripts. These scripts become a bottleneck, a maintenance headache, and a hidden source of technical debt.
But what if you could just declare the shape of the data you need and have an intelligent service handle the rest? What if you could turn complex data pipelines into simple, version-controlled API calls?
This is the promise of Intelligent Data Transformation as Code, a new paradigm powered by agentic workflows. With transform.do, you get more than a tool; you get an autonomous data engineer on demand.
For years, data transformation has been a manual, imperative process. You write a script that says:
This approach is fragile. If the source schema changes slightly, your script breaks. If the data volume explodes, your script grinds to a halt. It's procedural, hard to read, and even harder to maintain.
An agentic workflow flips the script. Instead of telling the computer how to perform the transformation step-by-step, you provide it with a declarative set of rules. You define what you want the final data to look like.
At transform.do, an AI agent acts as your executor. It takes your rules, intelligently interprets them, and performs the optimal sequence of operations to achieve the result. This isn't a chatbot—it's a specialized service agent designed to understand and execute data operations with peak efficiency.
The benefits are immediate:
Let's see how simple this is. Imagine you're ingesting user data from a legacy system and need to prepare it for your modern application's API.
With the @do/sdk, you can define and execute this entire workflow in a few lines of code.
import { Agent } from "@do/sdk";
// Initialize the transformation agent
const transform = new Agent("transform.do");
// Define your source data and transformation rules
const sourceData = [
{ "user_id": 101, "first_name": "Jane", "last_name": "Doe", "join_date": "2023-01-15T10:00:00Z" },
{ "user_id": 102, "first_name": "John", "last_name": "Smith", "join_date": "2023-02-20T12:30:00Z" }
];
const transformations = {
targetFormat: "json",
rules: [
{ rename: { "user_id": "id", "first_name": "firstName", "last_name": "lastName" } },
{ convert: { "join_date": "date('YYYY-MM-DD')" } },
{ addField: { "fullName": "{{firstName}} {{lastName}}" } }
]
};
// Execute the transformation
const result = await transform.run({
source: sourceData,
transform: transformations
});
console.log(result.data);
What just happened here?
The output would be:
[
{ "id": 101, "firstName": "Jane", "lastName": "Doe", "join_date": "2023-01-15", "fullName": "Jane Doe" },
{ "id": 102, "firstName": "John", "lastName": "Smith", "join_date": "2023-02-20", "fullName": "John Smith" }
]
This is ETL as Code—powerful, elegant, and incredibly easy to manage.
A simple transformation is great, but real-world data pipelines are complex. transform.do is built to handle them with ease.
It's time to retire those brittle, snowflake ETL scripts. By embracing an agentic, code-first approach, you can build data workflows that are robust, scalable, and a pleasure to maintain. Let an AI agent handle the tedious execution so you can focus on what truly matters: the data itself.
Ready to automate your data transformation? Visit transform.do to get started and deploy your first agent in minutes.
Q: What kind of data transformations can I perform?
A: You can perform a wide range of transformations, including data mapping (e.g., renaming fields), format conversion (JSON to CSV), data cleansing (e.g., standardizing addresses), and data enrichment by combining or adding new fields based on existing data.
Q: Which data formats are supported?
A: Our platform natively supports the most common data formats, including JSON, XML, CSV, and YAML. Through our agentic workflow, you can also define handlers for proprietary or less common text-based and binary formats.
Q: How does transform.do handle large datasets or ETL jobs?
A: Our platform is built for scale. Data is processed in efficient streams, and workflows can run asynchronously for large datasets. You can transform terabytes of data without blocking your own systems and receive a webhook or notification upon completion.
Q: Can I chain multiple transformations together?
A: Yes. A transformation workflow on .do is a service with a stable API endpoint. This allows you to chain multiple transformations together or integrate them with other services to build complex, multi-step data processing pipelines, all defined as code.