AI-Based Data Engineering
Build data platforms where AI participates in the full lifecycle, generating code, writing documentation, and triaging failures, while humans stay in charge of intent, definitions, and accountability. Grounded in one running case study built on Snowflake.
What it covers
Most data engineering books stop at moving bytes. This one starts where the hard problems live: coordinating meaning, managing change, and maintaining trust at the pace the business moves, while AI agents consume that same data at scale.
It builds a complete platform from the ground up: context pipelines that give models what they need to reason correctly, structured generation that constrains what they can produce, and evaluation that catches failures before production. Every pattern is applied to OpsPulse, the same case study across all 14 chapters. The platform starts at an AI-readiness score of 4 out of 18 and ends at 18.
Stack: Snowflake, Cortex, Anthropic Claude API, dbt, Airflow, MCP, GraphRAG, OpenLineage, EU AI Act
The problem OpsPulse starts with
Four teams, four definitions of "active customer," four numbers from the same data. Each chapter resolves one more dimension of this, until the platform can answer the question with a single, traceable, reproducible number.
Chapters code for every chapter
The score under each chapter is the OpsPulse readiness at the end of it.