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Databases

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  1. Build Your Own Database in Go From Scratch
    Build Your Own Database in Go From Scratch
    From B+tree to SQL in 3000 lines
    build-your-own.org

    Learn databases from the bottom up by coding your own, in small steps, and with simple Go code (language agnostic).Atomicity & durability. A DB is more than files!Persist data with fsync.Crash recovery.KV store based on B-tree.Disk-based data structures.Space management with a free list.Relational DB on top of KV.Learn how tables and indexes are related to B-trees.SQL-like query language; parser & interpreter.Concurrent transactions with copy-on-write data structures.

  2. Moderne Datenzugriffslösungen mit Entity Framework Core 10.0
    Moderne Datenzugriffslösungen mit Entity Framework Core 10.0
    Datenbankprogrammierung / Objekt-Relationales Mapping mit C# in .NET 10.0
    Dr. Holger Schwichtenberg

    Dies ist die top-aktuelle Version 14.9 dieses Fachbuchs vom 16.10.2026 auf dem Stand Entity Framework Core 10.0.13.

  3. Rusty Graphs - AI Ready Graphs for Rust Developers

    Language models guess. Knowledge graphs know. Rusty Graph shows you how to build a local AI agent whose memory is a real knowledge graph — typed, validated, reasoned over, and queryable with SPARQL — all inside a single static Rust binary. No JVM. No Docker sidecar. No Python runtime bolted to the side. You will build Ares a research-assistant agent that observes papers, forms beliefs, makes promises to other agents, and tracks the provenance of every fact it holds. Chapter by chapter, Ares grows from an empty Cargo workspace into a full pipeline: > load → reason → validate → query → answer All of it in under a thousand lines of idiomatic Rust, using three crates that actually work today: `oxigraph`, `reasonable`, and `rudof_lib`. `grapfeo` You will learn how to- Model a domain as RDF triples and load them into an embedded store. - Write RDFS and OWL 2 RL axioms that infer trust, identity, and inverse relationships — automatically. - Guard your graph with SHACL shapes that reject bad data at the boundary, not in production. - Query everything with SPARQL, from simple lookups to federated queries across named graphs. - Wire the graph into a hybrid RAG pipeline so your LLM answers are grounded in facts, not vibes. Who it is forRust developers building agents, assistants, or any system where the answer "the model said so" is not good enough. You should be comfortable with Cargo and traits. You do **not** need any prior semantic-web background — every concept is introduced through Ares before any formal definition appears. Why Rust, why nowLocal agents are the next deployment target: a user's laptop, a Raspberry Pi, a WASM sandbox. Python cannot go there comfortably. Rust can. This book is the missing manual for the Rust side of the semantic web — the one that tells you exactly which crates work, where the ecosystem is thin, and how to ship anyway. Stop hoping your model tells the truth. Give it a graph that does.

  4. Spatial Data Management with DuckDB
    Spatial Data Management with DuckDB
    From SQL Basics to Advanced Geospatial Analytics
    Qiusheng Wu

    Unlock the power of DuckDB for modern geospatial analytics. This hands-on guide helps GIS professionals master efficient spatial data management, transforming massive real-world datasets into powerful insights using SQL, Python, and DuckDB’s spatial extension. Full-color print edition is available on Amazon.

  5. High-Performance Java Persistence
    High-Performance Java Persistence
    Get the most out of your persistence layer
    Vlad Mihalcea

    A high-performance data access layer must resonate with the underlying database system. Knowing the inner workings of a relational database and the data access frameworks in use can make the difference between a high-performance enterprise application and one that barely crawls.

  6. GraphDuck : duckdb for embedded Ai agents and graphs

    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.

  7. TypeDB for Edge AI Agents
    TypeDB for Edge AI Agents
    Volodymyr Pavlyshyn

    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.

  8. PostgreSQL From MVCC to Production

    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.

  9. How Query Engines Work
    How Query Engines Work
    An Introductory Guide
    Andy Grove

    This book provides an introduction to the high-level concepts behind query engines and walks through all aspects of building a fully working SQL query engine in Kotlin.

  10. Mastering Qdrant for RAG Applications
    Mastering Qdrant for RAG Applications
    Building Production Vector Search Systems with the Open-Source Vector Database
    Steve Publications

    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.

  11. Build a SQL Database Engine in C++
    Build a SQL Database Engine in C++
    Through Challenges --- Storage, Query Processing, Joins, Indexes, Vectorized Execution, and Crash Recovery (Complete, Units 1-14)
    Hatem M.

    Build a working SQL database engine in C++20 -- from raw pages to joins,indexes, vectorized execution and crash recovery -- one compliable, measuredchallenge at a time. Every claim is a benchmark you run yourself; every unitends with a real bug, caught and fixed. The finale holds the engine toPostgreSQL and SQLite. Complete: all 14 units. Nothing asserted.

  12. System Design for the LLM Era
    System Design for the LLM Era
    Patterns and Principles for Production-Grade AI Architecture
    Sampriti Mitra

    STOP building fragile AI wrappers. START designing resilient AI systems. Lots of companies are trying to make their small AI experiments into big products, but they don't have a good plan. Engineers need a practical guide to build these new AI systems the right way - so they can handle scale, be reliable, and won't cost too much. This book is that guide. It explains how to design systems that use AI models. This book breaks down the architecture of real AI applications, like an AI-powered code editor or a smart learning app. It gives you a deep, practical look at the real-world challenges and solutions for building these systems. It discusses system design concepts for systems that use LLMs.

  13. Rust Projects - Write a Redis Clone
    Rust Projects - Write a Redis Clone
    Explore asynchronous programming with the actor model using Rust and Tokio
    Leonardo Giordani

    Explore the power of Rust with "Rust Projects: Write a Redis Clone". This hands-on guide takes you through building a Redis-inspired database from the ground up, introducing key programming concepts like TCP connections, the RESP protocol, and concurrency. Following the CodeCrafters challenge, this book gradually builds your skills, making complex topics accessible. Whether you're new to Rust or looking to deepen your understanding, this project-based journey offers practical, real-world insights into modern systems programming. The book contains 40% discount code for CodeCrafters.io!

  14. Anatomía de una consulta SQL
    Anatomía de una consulta SQL
    Un Viaje a Través del Procesamiento de Consultas de PostgreSQL
    Jesús Espino

    ¿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.

  15. Deep dive into a SQL query
    Deep dive into a SQL query
    A Journey Through PostgreSQL's Query Processing
    Jesús Espino

    What really happens when PostgreSQL executes your query? Follow a SQL statement through every stage of PostgreSQL's internal pipeline—from raw text to returned results—and gain the deep understanding that transforms how you write, tune, and debug database applications.