Using AI Agents for Data Engineering and Data Analysis
Published by Self-published · Tech
A practical guide to leveraging AI agents for automating data engineering pipelines and performing intelligent data analysis.
Who this book is for
Data engineers, data analysts, and analytics engineers.
Table of contents
- Why AI Agents Matter for Data Teams
- Setting Up Your Environment
- Anatomy of an Agent Interaction
- Schema Design and DDL with Agents
- Data Ingestion and Transformation
- Building a Semantic Layer with Agents
- Query Optimization and Reflections
- Data Quality and Governance
- Exploratory Analysis with Agents
- AI SQL Functions in Practice
- Reporting and Visualization Workflows
- Ad-Hoc Investigation and Troubleshooting
- MCP Server and External Agents
- Building Agent Skills for Dremio
- Automation and Pipelines with Agents
- What Comes Next
Excerpt
Data teams spend most of their time on work that follows predictable patterns. Writing DDL. Building views. Debugging a query that returns unexpected results. Classifying a column. Summarizing a table. These tasks require knowledge and judgment, but the mechanical steps are repetitive. AI coding agents can handle the mechanical part. This book teaches you how to use AI coding agents for data engineering and data analysis. Not in theory. In practice, with real tools, real SQL, and a real data platform.
