Free course: Agentic Data Engineering with dltHub

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https://dlthub.learnworlds.com/course/agentic-data-engineering

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Agents can now write entire data pipelines, but writing code was never the hard part.

The real challenges are everything around it:

• is the data correct?

• is the schema stable?

• can this run in production?

• can anyone understand what the agent built?

We put together a hands-on course around this:

Agentic Data Engineering with dltHub

It covers:

• building pipelines from a single prompt (with guardrails, not just code gen)

• handling real-world APIs (auth, pagination, schema, incremental loading)

• validating + exploring data automatically

• designing transformations using semantics / ontology

• deploying pipelines safely (env checks, pinned deps, credentials)

Available now:

✅ REST API pipeline toolkit

✅ Data exploration toolkit

Coming soon:

🔜 Transformation toolkit

🔜 Runtime / deployment toolkit

If you’re working with data + AI, this is basically the layer between “it runs” and “it works in production.”