Can AI Write Your ETL? Experimenting with Generative Data Mapping

Data transformation is one of the unsung, often grueling, tasks of modern software development. Whether you're migrating a legacy database, integrating with a third-party API, or feeding a data warehouse, you inevitably face the challenge of reshaping data from a source format into a target format. This process, a core part of any ETL (Extract, Transform, Load) pipeline, is notoriously manual, time-consuming, and prone to error.

At transform.do, our mission is to simplify this complexity. We believe in Intelligent Data Transformation as Code, turning brittle scripts into robust, version-controlled services. But we're constantly asking ourselves: can we simplify it even more?

What if you didn't have to write the mapping rules by hand? What if an AI could analyze your source and target data structures and generate the transformation logic for you? This is the question driving our latest research into generative data mapping.

The Tedium of Manual Data Mapping

Before we dive into the future, let's acknowledge the present pain. Imagine you need to map user data from a monolithic system to a new, sleek microservice.

The source data might look like this:

Your new service expects this structure:

Manually writing the code for this involves:

• Renaming: user_id to id, first_name to part of fullName.
• Type Conversion: Converting the user_id integer to a string.
• Format Conversion: Changing the join_date from a full ISO 8601 timestamp to a YYYY-MM-DD string.
• Data Enrichment: Combining first_name and last_name into fullName.
• Logic Mapping: Converting the string "ACTIVE" to the boolean true.

This is a simple example. Real-world scenarios involve dozens or hundreds of fields, nested objects, and inconsistent source data. Each rule is a potential point of failure. This is where transform.do already helps by defining these steps as a clear, declarative workflow. But what if we could automate the creation of the workflow itself?

Our Hypothesis: Generative AI for Data Transformation

The recent leap in the capabilities of Large Language Models (LLMs) has opened up new possibilities. These models excel at understanding patterns, context, and structure—not just in natural language, but in code and data as well.

Our hypothesis is this: By providing an AI model with a sample of the source data and a schema (or sample) of the target data, it can infer the necessary transformation steps and generate the rules automatically.

This is the core idea of generative data mapping. It's not just about matching field names; it's about understanding semantic intent. An AI can infer that user_id and id represent the same entity, that join_date and signupDate are semantically equivalent, and that first_name and last_name should be combined to create fullName.

An Experiment in Action

At transform.do, we're building this capability directly into our agentic workflow. Imagine an "AI Mapper" agent that you can invoke. You provide it with your source and target, and it returns a fully-formed transformation ruleset.

Let's revisit our example. We'd feed the AI agent our source and target structures. The AI would analyze them and produce a transform.do configuration like this:

This generated output is not just a one-to-one mapping. It's a complete, executable set of instructions that represents a sophisticated understanding of the transformation required. This is ETL as code, generated intelligently. The AI correctly inferred the need to concatenate names, reformat the date, convert types and values, and clean up the original fields.

Challenges and the Human-in-the-Loop

Of course, this isn't magic. The road to fully autonomous ETL is paved with challenges:

1. Ambiguity: What if a source field could plausibly map to multiple target fields? The AI needs a way to handle ambiguity, perhaps by suggesting multiple options and asking for user clarification.
2. Complex Business Logic: While AI can handle common patterns, highly specific business rules (if user is from 'US' and signup is before 2022, apply 5% discount) will still require human oversight and definition.
3. Trust and Verification: The goal is not to blindly trust the AI. The generated rules are human-readable and version-controllable for a reason. The AI acts as a powerful assistant, generating a high-quality first draft that a developer can quickly review, test, and approve.

The Future of ETL is Intelligent

Generative data mapping represents a fundamental shift in how we approach data transformation. It promises to eliminate the most tedious and error-prone aspects of building data pipelines, freeing up developers to focus on higher-level business logic and architecture.

By integrating this intelligence into the transform.do platform, we're taking the concept of ETL as a Service to its logical conclusion. It's a future where robust, scalable data pipelines can be defined through a simple API and refined by an intelligent AI agent. The result is faster development cycles, more resilient systems, and more powerful data workflows.

While this research is ongoing, it's a core part of our vision for the future of data transformation—a future that is not just automated, but truly intelligent.

Ready to simplify your data transformations today? Explore transform.do and see how our API-first approach to ETL as code can streamline your data pipelines.