From Claude to Pipelines: AI-Assisted Data Engineering

​You already rely on your AI coding assistant to write and maintain your software. Your Cursor and your Claude Code understand repositories, modify code safely, and iterate quickly with human oversight.

​What about bringing you data to your AI coding platform? Data pipelines touch shared state, historical data, and downstream systems. Running AI-generated changes against real data requires stronger guarantees than most data platforms provide by default.

​In this webinar, we show how to use your AI coding assistants to operate on data pipelines the same way they operate on software repositories.

• ​Use the MCP server to give AI coding assistants a safe, fully code-addressable interface to inspect data, run pipelines, validate results, and propose changes.

• ​Every data operation runs on an isolated branch, executes transactionally, and publishes only when validated.

• ​Allow your AI tools to iterate directly on production data without relying on staging environments, ad-hoc sandboxes, or brittle conventions.

​We will walk through a concrete workflow where an AI coding assistant proposes changes to a data pipeline, executes those changes on real data, inspects the results, and publishes them as a single atomic update.

​What You’ll Learn

• ​How AI coding assistants can be used to evolve real data pipelines in your production environment.

• ​How to use branches and git-for-data for safe iteration on production data.

• ​How to run, validate, and publish AI-generated transformations as atomic changes.

• ​How to integrate AI tools into normal data engineering workflows without extra staging or glue.

​Format

• ​15 min – AI workflows in data engineering

• ​15 min – Live walkthrough: AI-assisted pipeline changes with Bauplan

• ​10 min – Q&A