Databases

Why This Book Is Unique·        Focused specifically on data science applications of SQL, not just traditional database operations.·        Includes Python integration, bridging database skills with modern data analysis.·        Covers NoSQL and unstructured data, expanding student exposure beyond relational databases.·        Emphasizes real datasets, case studies, and hands-on exercises, making learning interactive and practical.·        Prepares students for academic projects, internships, and entry-level data science roles.  

A typed, signed-deliverable playbook for the enterprise-scale Oracle 19c → Azure migration. Across fifteen chapters, thirteen signed JSON deliverables chain end-to-end from Migration Assessment Bundle to Decommissioning Readiness Report. Defensible at a code review. Sign-off-ready for a director.

Most analytics books teach techniques. Applied & Advanced Analytics teaches judgment. Designed for experienced analysts, this book develops the senior-level skills required to define complex problems, defend analytical decisions, operate at organizational scale, and remain accountable for what analytical work ultimately causes.

This book is for anyone who wants to feel more confident. Over eleven chapters, we put together a single sample application, and we watch it grow from a simple table used by one employee into a more complex system that's divided into different parts, has backups, is secure using OAuth, and is audited.

Most analytics books teach tools first and thinking later. Data Analytics Foundations reverses that order — building the judgment, technical skills, and communication discipline required to transform raw data into trusted decisions. From spreadsheets and SQL to data quality, visualization, and analytical reasoning, this book equips readers with a complete foundation for professional analytical practice.

Most data modelling books teach you the craft. This one teaches you the reality. Seven chapters of honest, practical guidance from real project experience — covering conceptual modelling, governance, enterprise challenges, and the human side of data work that nobody else writes about.

Postgres woke you up at three in the morning. Same code. Same hardware. Something changed. This book explains what changed and why, starting from the one idea that makes everything else legible: MVCC.

Most developers treat Hibernate as a black box that turns objects into rows. This book opens the box, shows you exactly what happens inside, and teaches you how to make it work for you at scale.

¿Qué ocurre realmente cuando PostgreSQL ejecuta tu consulta? Sigue una sentencia SQL a través de cada etapa del pipeline interno de PostgreSQL —desde el texto sin procesar hasta los resultados devueltos— y adquiere ese conocimiento profundo que transformará la forma en que escribes, optimizas y depuras aplicaciones de bases de datos.

Learn Polars, the pandas killer for data analysis.

You don't need a graph database. You need graph thinking inside DuckDB. GraphDuck takes you from SQL adjacency lists to metagraphs, hypergraphs, and hybrid Graph RAG pipelines — all inside DuckDB. Learn to model knowledge graphs, build AI agent memory systems, run graph algorithms, and combine vector search with graph traversal in a single embedded database. Every concept comes with runnable code. No infrastructure required.

Your AI agent is only as smart as what it remembers. Most developers treat knowledge representation as an afterthought -- a data structure problem, not an architecture problem. They flatten complexity into schemas that can't express relationships, deploy embeddings without grounding, and build agents that degrade under their own reasoning load. This book changes that. **TypeDB for Edge AI Agents** is a practitioner's guide to building knowledge systems that actually work at the scale and complexity your agents demand. TypeDB is built on type theory -- the same mathematics that powers formal verification and programming language safety. For agents, that means you can express constraints that prevent bugs in your knowledge layer, reason about what's possible and what's forbidden, and build memory systems that don't require constant hallucination detection. With the Rust rewrite in TypeDB 3.0, your agents can carry sophisticated knowledge graphs on-device -- no round-trip to a server, no latency, no external dependencies. This book covers everything from the PERA data model and OWL ontologies to promise graphs for multi-agent coordination, giving you the patterns to design knowledge systems that scale without becoming incoherent. Whether you're an ML engineer, AI architect, or backend developer building production agent systems, this book bridges the gap between type theory and working code. You'll learn to design knowledge graphs that don't decay, structure agent memory that doesn't degrade over time, and coordinate multi-agent systems that respect causality and distributed constraints. Stop building agents that confidently hallucinate -- start building agents that reason correctly, remember reliably, and coordinate with certainty.

Metagraphs for Agentic AI: Beyond Triples, Beyond HypergraphsFrom Knowledge Graphs to Knowledge ArchitecturesThe triple is not enough.Every AI engineer building agent memory hits the same wall. You model a meeting as a knowledge graph triple — and immediately lose the fact that five people were in the room, a decision was made, and that decision caused three downstream actions. You reify. You flatten. You create workarounds. And your "knowledge graph" becomes a tangle of auxiliary nodes that machines can traverse but no human can reason about.This book shows you the way out. What You'll Learn Metagraphs are graph structures where edges connect sets of nodes to sets of nodes — and where edges themselves can be referenced as first-class nodes. They are the missing data structure for AI agents that need to remember, reason, and coordinate like humans do.This book takes you on a complete journey:Hypergraphs first. You'll learn what they are, why they matter, and where they break down. You'll implement them three ways — in SQL, in LadybugDB (Cypher), and in TypeDB — so you understand the tradeoffs viscerally, not just theoretically.Then metagraphs. You'll see how metagraphs solve the fundamental hypergraph problem (edges that can't be nodes), explore RDF named graphs as a lightweight metagraph, and implement full metagraphs in the same three database paradigms with production-ready, commented code.Then the big ideas. Semantic Spacetime. Holonic systems. Human cognitive architecture mapped to graph structures. Multi-agent coordination. Promise Theory for autonomous AI networks. This is where metagraphs stop being a data structure and become an architecture for intelligence. Who This Book Is For You're a software engineer, AI researcher, or knowledge graph practitioner who builds real systems. You've used Neo4j or RDF stores. You've built RAG pipelines. You've felt the limits. You want to know what comes next.No PhD required. Every concept comes with working code in SQL, Cypher, TypeQL, SPARQL, and Python. What Makes This Book Different This isn't a theoretical monograph. It's the distillation of two and a half years of research, 130+ published articles, and hands-on implementation at the intersection of knowledge graphs and agentic AI.Every chapter bridges theory and practice. You'll read about Basu and Blanning's formal metagraph definition — and then build the schema in PostgreSQL. You'll learn Mark Burgess's Promise Theory — and then model a multi-agent coordination protocol as a six-layer promise graph. You'll understand why labeled property graphs are secretly metagraphs — and what that means for your Neo4j deployment today. 18 Chapters. Three Parts. One Argument. Part I — The Hypergraph Foundation (7 chapters): From the knowledge representation crisis through hypergraph theory to three complete database implementations.Part II — The Metagraph Solution (5 chapters): Metagraphs as the answer, RDF named graphs as a bridge, and three full metagraph implementations with detailed commentary.Part III — Theory Meets Practice (6 chapters): Semantic Spacetime, labeled property graphs, AI memory and human cognition, holonic systems, agent-to-agent interaction, and Promise Graphs for network-of-networks coordination. The Core Thesis If you want AI agents that reason like humans, you need knowledge structures that capture how humans actually organize knowledge — not as flat collections of facts, but as nested, hierarchical, context-rich, temporally-aware structures where relationships themselves carry meaning and can be the subject of further reasoning.Metagraphs are that structure. This book shows you why, and how to build with them.

Learn graph databases the hands-on way — no servers, no setup, no fluff.Hands-On LadybugDB Cypher takes you from your first MATCH to complex recursive queries, shortest-path algorithms, and real-world AI agent graphs — all running locally in under a minute. 27 chapters. One evolving project. Every Cypher concept you need. Start querying graphs today.

Stop being a syntax typist. Master Clean Code Architecture and Algorithmic Thinking to become a professional Software Architect in 2026. This blueprint teaches you to build Scalable Systems using Binary Logic, Big O Notation, and proven Software Engineering Patterns. Perfect for the modern Backend Developer, self-taught coder, and Tech Entrepreneur looking to design high-performance software from scratch.