The Book on Data, Data Engineering, Data Analytics, and Agentic AI - Volume 1
Published by Self-published · Tech
A complete guide to modern data systems, data engineering, analytics, and AI.
Who this book is for
Data engineers who want to build resilient pipelines, software engineers moving into data, and technical leaders who need one reference for data infrastructure.
Table of contents
- The Origins of Data Management
- From OLTP to OLAP and the Birth of the Data Warehouse
- The Data Lake Era
- The Data Lakehouse Architecture
- Understanding Data Types and Structures
- Data Modeling for Analytics
- Essential Statistics for Data Practitioners
- File Formats and Storage
- Open Table Formats
- The Open Source Data Ecosystem
- The Data Engineering Challenge
- ETL, ELT, and Data Pipeline Design
- Data Warehouse Architectures
- Data Lakehouse Architectures
- Query Federation and Virtual Data Layers
- The Medallion Architecture and Data Mesh
- Batch Processing at Scale
- Stream Processing and Real-Time Pipelines
- Single-Node Engines: DuckDB, DataFusion, and Polaris
- Designing for Cost, Performance, and Maintainability
Excerpt
Every significant shift in computing follows the same pattern. First, the early adopters build something that works but is hard to use. Then, the tooling matures, the costs drop, and suddenly an entire industry reorganizes around the new approach. We saw it with relational databases in the 1980s, with the web in the 1990s, with cloud computing in the 2010s, and we are living through it right now with two simultaneous revolutions: the open data lakehouse and artificial intelligence.
