Building Data Engineering Code with Agentic AI
Build real-world data engineering code through agentic features of Amazon Q Chat
Course Summary
This short, use-case–driven course demonstrates how data engineering code can be built iteratively and confidently using agentic, human-in-the-loop workflows with Amazon Q Chat.
Instead of focusing on deep Python or PySpark expertise, the course shows how to:
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Translate a real-world data engineering use case into executable logic
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Collaborate with Amazon Q Chat to generate, refine, and correct code iteratively
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Use conversational feedback to improve transformations, structure, and error handling
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Move from exploratory development in a notebook to a reusable execution-ready script
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Focus on problem-solving and engineering thinking, rather than syntax memorization
The emphasis is not on tools or services, but on how data engineers can work with AI as a collaborator to build real solutions faster and with greater confidence.
This course is designed to supplement
RADE™ Agentic Data Engineering with Amazon Q and
RADE™ AWS Data Engineering Labs by showing agentic workflows applied to real code creation.
Course Curriculum
Sachin Chandrashekhar
Course Pricing
RADE Building Data Eng Code with Agentic AI
$100 USD
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Build real-world data engineering code through agentic features of Amazon Q Chat
