Idézet:While much of data engineering still means writing repetitive code, stitching together orchestration tools, and manually validating systems, the emergence of AI-assisted development is fast transforming this work. Redefining Data Engineering with AI introduces the concept of agentic engineering, a structured approach in which engineers express intent in natural language, and AI generates pipelines, documentation, test suites, and monitoring. Engineers remain in control, focusing on big-picture design, governance, and quality while delegating routine implementation to AI.

This go-to guide uses realistic case studies to show how large language models can support each phase of a data project. Through a guided build of a patient search platform, you'll learn how to design systems that integrate AI responsibly and effectively. Each chapter leads you through a practical step in the lifecycle, illustrating exactly where AI adds value and where human input is most crucial.

Describe business goals and translate them into pipeline-ready assets
Generate code, test cases, and documentation using structured prompts
Integrate LLMs and governance into modern data workflows
Automate observability and reduce operational drift with AI-driven monitoring
Create secure data products while preserving compliance and quality
Adopt a repeatable, intent-driven framework for scalable data practices

Idézet:? Redefining Data Engineering With AI 0642572283919.epub (Ashok Singamaneni, Sarath Chandra Bandaru, Phani Vemuri, and Aditya Chaturvedi) (2026) (1.94 MB)

Idézet:A kódrészlet megtekintéséhez be kell jelentkezned, vagy nincs jogosultságod a tartalom megtekintéséhez.

Idézet:A kódrészlet megtekintéséhez be kell jelentkezned, vagy nincs jogosultságod a tartalom megtekintéséhez.