The Book on Data, Data Engineering, Data Analytics, and Agentic AI - Volume 2
Published by Self-published · Tech
A complete guide to SQL analytics, BI, and agentic AI for modern data platforms.
Who this book is for
Analysts who want to write performant SQL, data engineers bringing large language models into their work, and technical leaders planning an enterprise AI strategy.
Table of contents
- SQL Foundations: The Language of Data
- Intermediate SQL: Aggregations, Subqueries, and Set Operations
- Advanced SQL: Window Functions and Analytical Queries
- SQL Performance and Optimization
- SQL for Data Engineering
- Building Dashboards That Tell Stories
- Business Intelligence Tools and Platforms
- From Data to Insight: The Analyst's Workflow
- Building Data Stories and Reports
- Data Quality, Governance, and Trust
- The AI Revolution: From Neural Networks to Large Language Models
- Understanding LLM Architecture and Capabilities
- The Rise of Agentic AI
- Building AI Agents: Architecture and Patterns
- The Model Context Protocol (MCP)
- Agentic Coding Tools: Claude Code, Codex, and OpenCode
- AI for Data Engineering
- AI for Data Analytics
- Enterprise AI and Analytics Platforms
- The Future of Data and AI
Excerpt
In 1970, a mathematician named Edgar Codd published a paper at IBM Research that would ultimately become one of the most consequential documents in the history of computing. The paper proposed organizing data into relations, or tables, and querying those tables using a declarative language. The idea was radical: instead of telling a computer how to retrieve data, step by procedural step, you simply described what data you wanted, and the engine figured out the mechanics on its own.
