Books

Passage 1 — Chapter 9, "Data Security": a definition that sets the technical tone immediately A hacker is someone skilled at finding undocumented techniques and loopholes in the tangled architecture of complex information systems; by intent, a "white hat" looks for such loopholes in order to strengthen the system, while a "black hat" uses them to steal or to cause harm. Passage 2 — Chapter 14, "Metadata Management": an image that explains the whole topic in one paragraph A vivid illustration: an enormous document archive with no index at all — the shelves are full, but there is no way to find out what sits on them short of examining every single box by hand. That is exactly what an organization looks like when it has piled up mountains of data without also taking care of metadata — the information physically exists, but it cannot be used systematically, since the only way to find what is needed is to already know where it sits. Knowledge about data is always scattered: in a large company, one person carries the structure of a single database in their head, another the rules of a single integration, a third the history of a single metric, and nobody holds the complete picture. Passage 3 — Chapter 15, "Data Quality Management, Part 1": why "quality" is an empty word without a yardstick A postal address missing an apartment number works perfectly well for a mass catalog mailing and works terribly for a courier who has to knock on the right door — and in both cases it is the very same row in the very same database, only the yardstick applied to it differs. Passage 4 — Chapter 21, "Organizational Change Management": the book's closing summary Data governance, the coordinating hub of eleven knowledge areas this book opened with, stays an empty frame until specific people come to value the new way of working with data through their own experience — which is exactly why a book that began by mapping the circle of disciplines around that hub fittingly closes not with another technique or tool, but with a conversation about the person without whose deliberate participation no structure ever becomes a practice.

PostgreSQL Under Load shows what really happens when production gets busy. Learn how connections, transactions, MVCC, locks, WAL and autovacuum interact under pressure, then use that knowledge to diagnose incidents, tune PostgreSQL and build systems that stay reliable when concurrency spikes.

Cloud GPU rental costs more than compute time. It can also introduce recurring expenses, external dependencies, and additional questions about where your prompts, datasets, and model artifacts are processed. A local machine learning workstation gives you direct control over the hardware, software stack, and data workflows. The Local AI Stack is a technical engineering reference for building a Windows-based machine learning workstation with Linux environments, NVIDIA GPU acceleration, and virtualization. Across 140 pages, it follows the stack from firmware configuration and Windows virtualization features to CUDA-enabled containers, model inference, storage optimization, and system recovery.

What if you could build a graph database the way SQLite is built to disappear into an application? This book takes you inside the engine, from storage pages and transactions to traversal, queries and crash recovery. With complete working code, you’ll build a fast, durable embedded graph database from the ground up.

Go from your first SQL query to solving real database problems with confidence. This hands-on guide covers everything from SQL fundamentals and data analysis to advanced queries, optimization and production best practices, with practical examples that work across today’s major database systems.

Supabase wired into Claude and Cursor over MCP, with RLS designed for agent access, service_role blast radius contained, and ten failure modes diagnosed.

Your query planner doesn't know how many rows will match. It guesses. This book shows you exactly how — and why it's sometimes wrong.

Go beyond the basics and learn how to get the most from RocksDB. This practical guide explains the architecture behind its speed, shows how to tune it for real workloads and helps you solve performance issues with confidence. Packed with code examples and production-focused advice, it is built for developers who need storage that scales.

DuckDB has changed how people work with data by bringing fast analytical queries to a lightweight embedded database. Whether you are exploring Parquet files, building data pipelines or embedding analytics into your application, this handbook shows you how to get the most out of DuckDB with practical examples and real-world techniques.

SQLite is everywhere but running it well in production takes more than flipping a few settings. This book goes beyond the basics to show how SQLite really works, then walks through the patterns, tools and tradeoffs behind reliable, high-performance deployments. Packed with practical examples, it's built for engineers who want confidence in production.

The bugs live in the data. From your first SELECT to migration testing, NoSQL, and GDPR-compliant masking — with exercises all the way.

An Oracle DBA handed a MySQL database is surprised twice: first by how little seems to be there, then by how much of what you know doesn't carry over. All 79 chapters translate what you already do in Oracle — architecture, execution plans, indexing, replication, failover — into the language of MySQL 8.4, with every result measured on real servers. Continuously updated.

Ship the NEXUS-1 backend for real: a genuine SQL Server on every test run, a dispatcher proven under a killed process, structured logs with a correlation id, and a CI gate that will not package a red build. Two hundred tests, one Dockerfile, one honest boundary.

Slow SQL queries waste time, increase costs, and limit scalability. Mastering SQL Performance Optimization teaches you how to analyze execution plans, optimize queries and indexes, and solve real-world performance bottlenecks across today's leading database platforms.

Mastering Qdrant for RAG Applications is your practical guide to building production-ready RAG systems with the leading open-source vector database. Learn how to design, optimize, and scale high-performance vector search using Qdrant through clear explanations, real-world examples, and hands-on code.