Books

"It’s 3 AM, your database just hit 500 connections, and everything has ground to a halt. Do you know why?"Most database outages aren't mysterious—they are architectural. Stop treating PostgreSQL as a black box and start understanding the mechanical "why" behind the engine. PostgreSQL Internals Mastery: Volume I is a modular deep dive designed for senior engineers who need to bridge the gap between writing SQL and architecting high-performance systems. From the process-per-connection model to the groundbreaking Asynchronous I/O (AIO) features of PostgreSQL 18, learn the internals that separate database architects from administrators. Don't just tune knobs; understand the machine.

这个一个用代码手搓数据库的项目。你可以通过这个项目：学习数据库底层原理和计算机基础。提升技术深度。通过实操来锻炼编程技能。获得一个完整的个人项目。可以用在简历、面试中。项目全面实现了几个最重要的部分：KV 储存引擎。SQL 与关系型数据库。索引与数据结构。虽然范围很广，但是被拆分成了多个小步骤。每个步骤都很简单，最多几十行代码。你会发现，复杂的概念可以从简单的概念演变而来，可以说是从0开始发明数据库。 作者网站上精选了一些类似的资源：程序员如何学习底层技术?可以邮件订阅作者网站。

Master AI Agents from Architecture to ProductionBuild autonomous agent systems that actually work in production. This comprehensive guide takes you from understanding ReAct patterns to orchestrating multi-agent systems at scale. What You'll Master:✅ Agent architectures: When to use agents vs RAG vs fine-tuning✅ Reasoning patterns: ReAct, Chain-of-Thought, Plan-and-Execute✅ Multi-agent orchestration with proper coordination protocols✅ Production deployment with error handling, monitoring, cost optimization✅ Tool calling, memory systems, and context management Who This Is For: Software engineers building LLM applications, backend engineers adding agentic capabilities, senior engineers preparing for AI agent interviews at top companies. What Makes This Different: 100+ production-focused scenarios with real architectural trade-offs. Real-world examples from companies shipping agent systems . Stop building chatbots. Start building agents that take action.

Master Generative AI from Theory to ProductionYou don't learn Gen AI from tutorials — you learn from solving real problems. How does ChatGPT handle context and avoid hallucinations? How does Perplexity build RAG at scale? How does GitHub Copilot generate accurate code? System Design Mastery - Generative AI teaches through real-world scenarios and production patterns. 116 scenario-driven case studies covering:✅ RAG with vector databases and hybrid search✅ Prompt engineering with Chain-of-Thought reasoning✅ Document processing with multi-format parsing✅ Multi-modal AI with vision and audio✅ Production deployment with monitoring and cost optimization Every scenario includes: production problem, architectural approaches, Gen AI patterns, decision frameworks, tool implementations, and interview-ready explanations. Learn through case studies from OpenAI, Anthropic, Google, Meta, and top AI-companies. Your journey from developer to Gen AI architect begins here — with scenarios you'll face and tools you can deploy.

Master Data and ML Systems at ScaleYou don't master data platforms from textbooks — you master from solving real problems at petabyte scale.System Design Mastery - Data Analytics and Machine Learning teaches through production scenarios and case studies. 100 scenario-driven case studies covering:✅ Lakehouse architecture with Delta Lake, Iceberg, Hudi✅ Real-time pipelines with Kafka, Spark, Flink✅ Feature stores with Feast for training-serving consistency✅ MLOps platforms with MLflow, SageMaker, Airflow✅ Data quality frameworks with Great Expectations✅ Multi-region data sync and model serving at scale Every scenario includes: production challenge, architectural trade-offs, data/ML patterns, decision frameworks, and interview-ready explanations. Learn through real-world case studies from Netflix, Uber, Airbnb, Spotify's petabyte-scale data and ML architectures. Your journey from data engineer to architect begins here — with scenarios you'll face and systems you can build.

You don't master distributed systems from diagrams — you master from solving complex problems at scale. How does Netflix handle distributed transactions across regions? How does Uber orchestrate sagas for ride workflows? System Design Mastery – Advanced Track teaches through production scenarios and case studies. 124 advanced scenario-driven case studies covering: ✅ Event sourcing, CQRS, saga orchestration✅ Service mesh with Istio configuration✅ Distributed tracing with OpenTelemetry✅ Workflow orchestration with Temporal✅ Change data capture with Debezium✅ Multi-region architectures and conflict resolution Every scenario includes: production challenge, architectural trade-offs, advanced patterns, decision frameworks, and interview explanations. Learn through real-world case studies from Google, Netflix, Uber, Meta, and Stripe's planet-scale architectures. Your journey from senior engineer to architect begins here — with scenarios you'll face and patterns you can implement.

Master System Design from Theory to ProductionYou don't learn system design from textbooks — you learn from solving real problems. How does Netflix handle video streaming at scale? How does Uber route millions of rides? How does Slack deliver messages instantly? System Design Mastery – Foundation Track teaches through real-world scenarios and case studies. 124 scenario-driven case studies covering:✅ Database design, sharding, replication✅ Caching with Redis configuration✅ Microservices with Spring Boot & Kubernetes✅ Message queues with Kafka✅ Load balancing with NGINX✅ Monitoring with Prometheus Every scenario includes: production problem, architectural approaches, design patterns, decision frameworks, tool configurations, and interview-ready explanations. Learn through real-world case studies — from Google, Amazon, Meta, Netflix, and top tech companies. Your journey from developer to designers begins here — with scenarios you'll face and tools you can deploy.

This reference volume consists of revised, edited, cross-referenced, and thematically organized articles from the Software Diagnostics and Observability Institute and the Software Diagnostics Library (former Crash Dump Analysis blog) about software diagnostics, root cause analysis, debugging, crash and hang dump analysis, and software trace and log analysis written from 15 April 2024 to 14 November 2025.

Build AI agents that truly remember, reason, and act—entirely on user devices. Move beyond prompt engineering to create autonomous systems with graph-based memory using SQLite and LibSQL. Learn to implement hypergraphs, metagraphs, and vector search for privacy-first AI that scales to millions of entities. From personal knowledge graphs to production mobile apps, master the three pillars of agent autonomy: tools, memory, and reasoning. Real code, working examples, battle-tested in production. The future of AI is local, private, and in your hands.

Your API deserves more than just working, it deserves to be exceptional. read more...

What's the difference between knowing about cybersecurity and actually doing cybersecurity? Practice with real tools on real problems. This field guide takes you from theory to practice with hands-on AWS, Linux, Python, Splunk, and SQL skills. Learn through actual incident response scenarios, not sanitized demos. Build working security solutions you can deploy tomorrow.

Data-architectuur in de praktijk is hét handboek voor elke data-architect én zijn omgeving. Wie dit boek gelezen heeft is thuis in termen als: data-principes en -patronen, datamodellen en repositories.

ہر بڑی ایپلیکیشن کے پیچھے، ایک منفرد کہانی چھپی ہوتی ہے جو ہمیں بتاتی ہے کہ کس طرح اُس نے اپنے آغاز سے لے کر کروڑوں اور اربوں صارفین کو ہینڈل کرنے کے لیے اپنے سسٹم کو بہتر بنایا۔ اس میں، ہم چند مشہور پلیٹ فارمز کی سکیلنگ کی کہانیوں پر نظر ڈالیں گے اور دیکھیں گے کہ انہوں نے کن چیلنجز کا سامنا کیا، کن ٹیکنالوجیز کا استعمال کیا، اور کس طرح اپنے آرکیٹیکچر کو بدلتے وقت اور بڑھتی ہوئی ضروریات کے مطابق ڈھالا۔ یہ کہانیاں نہ صرف بڑی کمپنیوں کے کامیاب سسٹمز کی گواہی ہیں، بلکہ ہمیں یہ سیکھنے کا موقع بھی دیتی ہیں کہ ہم اپنے پروجیکٹس کو کس طرح بہتر بنا سکتے ہیں، چاہے وہ چھوٹے پیمانے کے ہوں یا بڑے۔ ان سکیلنگ کی کہانیوں میں جھانکنے کا مقصد، دراصل سافٹ ویئر انجینئرنگ کے بہترین اصولوں اور غیر معمولی ڈیزائن کے فیصلوں کا جائزہ لینا ہے

Engineering Leadership in Regulated Environments: A CTO’s HandbookBy Chrysovalantis D. Koutsoumpos In regulated industries, engineering leaders face a unique challenge: how to build systems that move fast without breaking rules, and scale teams without losing clarity or control. This book offers practical guidance drawn from real-world experience — designed for engineering leaders who operate in environments where compliance, trust, and delivery must coexist. Covering 30 focused chapters, it explores topics like team structures, architecture, risk management, audit readiness, product delivery frameworks, and long-term strategy. Each chapter includes actionable insights, tools, and approaches that help bridge the gap between technical leadership and regulatory expectations. Whether you're leading engineering at a fintech, healthcare company, or any compliance-heavy organization, this handbook provides a thoughtful and pragmatic reference to support your growth — and your team’s.