Books

Learn how to implement DDD, CQRS and Event Sourcing. Understand the theory and put it into practice with JavaScript and Node.js. Utilize an extensive source code bundle and an interactive execution feature for a hands-on experience.

Master Domain-Driven Design Tactical patterns: Entities, Value Objects, Services, Domain Events, Aggregates, Factories, Repositories and Application Services; with real examples in PHP. Explore the advantages of Hexagonal Architecture and understand Strategic design with Bounded Contexts and their integration through REST and message queues.

“Happily purchased. Handy to have these in one place. Thank you!” — Kent Beck “Excellent new thinking on Domain-Driven Design. It's full of real practical experience in getting the most value from domain modelling. Just like the Eric Evans' DDD book, this gives more insight each time you read it.” — Ian Russell

Turn your engineering team into a force multiplier, not a bottleneck. This book gives current and aspiring engineering managers practical frameworks, questions, and examples for everything from one on ones and feedback to scaling teams and navigating crises.

RAG, Agent Bricks, the Multi-Agent Supervisor with MCP, Lakebase, MLflow 3, Lakehouse Monitoring, Feature Store, Vector Search. Every AI surface Databricks shipped at GA in 2025 and 2026, taught by a practitioner, current to 2026. What you will learn - Build RAG pipelines with Vector Search, embedding models, and citation grounding- Ship Agent Bricks for classification and information extraction- Orchestrate specialist agents with the Multi-Agent Supervisor and MCP- Use Lakebase as the operational Postgres layer for AI apps and agents- Detect data and model drift with Lakehouse Monitoring; wire alerts to retraining- Manage the ML lifecycle with MLflow 3 and the UC Model Registry- Govern features across training and serving with Feature Store (offline + online)- Serve foundation and custom models with AI Gateway controls Who this book is for Data engineers, ML engineers, and AI/ML architects who know PySpark and the Databricks platform and now need to ship production AI. Volume 3 is the recommended prerequisite. Table of Contents 1. Databricks SQL in Production. Warehouses, materialized views, three latency signals (admission, compilation, execution), the full dashboard backend wiring.2. External BI: Tableau, Power BI, dbt. Performance tips that take a dashboard from sluggish to instant, dbt configuration at incremental scale, the seam between BI and the lakehouse.3. AI/BI Dashboards. Anatomy of a Lakeview dashboard, draft vs published flow, the Dashboard Agent's reliable patterns, the five-grant permission model.4. Genie: Natural-Language Analytics. Grounding sources, the priority rule, the SQL Genie actually writes, the questions Genie answers cleanly versus the ones that confuse it.5. AI SQL Functions. ai_query, ai_parse_document, ai_extract for PDFs and HTML, univariate forecasts, the daily cost math for production AI SQL pipelines.6. Model Serving. Endpoints, the three fields that decide capacity and cost, the chat-completion payload, the five moving pieces of a production recommender.7. Foundation Models. Five major providers, the External Models config, the vendor-swap pattern (Claude to Gemini in hours, not weeks), the three habits that keep swap cost low.8. Vector Search and RAG. Six delta-sync arguments, three chunking strategies compared, the RAG function your app imports, end-to-end answer evaluation with traces.9. MLflow 3 and UC Model Registry. Versions, aliases, tags (and what each is not for), five tracking calls and what each one writes, the experiment-to-production lifecycle.10. Feature Store. Why SDP is the right producer, the six-file project layout, four parity-failure classes between offline and online stores and what causes each.11. MLOps as a Practice. Seven sources every incident reads from, three deploy patterns (canary, shadow, blue-green), three retrain strategies, five golden signals for an ML endpoint.12. Lakehouse Monitoring: Drift Detection. Six monitor parameters, the loop from drift alert to retraining, what to do when the baseline table is missing.13. Distributed Deep Learning. Three signals that force distributed training, picking the flavor (data, model, hybrid) from the bottleneck, four pieces of GPU memory worked out for a 7B model.14. Agent Bricks. Declarative classification and information-extraction agents, eval-set ingredients, the pre-compute pattern that makes small seed sets work.15. Multi-Agent Supervisor and MCP. The supervisor build, synthetic-turn evaluation, three real conversations end to end, the auth-passthrough chain across child agents.16. Lakebase: Operational Postgres for AI. Five alternatives compared, sub-10ms reads for AI apps, the lineage from Delta source through SDP into Postgres and onward to the endpoint.17. Capstone: Retail Intelligence App. Ten stages, each anchored to an earlier chapter. The smoke test that confirms every stage of the platform is reachable, the new-data path through the recommender.18. Certification and What's Next. The certification paths that actually map to the book, and the reading list the on-call team uses when something breaks.

We can find a solution to every problem – if we can agree on what exactly the problem is. Understanding problems and communicating requirements is the subject of this book. Many different terms are used for this (business analysis, system analysis, requirements engineering, etc.) and there are many job titles for those involved. Business analysis and requirements engineering are two sides of the same coin: as an entrepreneur, you want to streamline your organizational and operational structures and make them more effective. As a product developer, you want to find unique selling points for your existing or new products, whether it's price, quality, or performance. As an IT department, you want to understand these requirements in order to deliver great products and systems. This book presents a pragmatic and agile approach to dealing with requirements. It provides you with methods, notations, and many practical tips for effectively handling requirements between clients and contractors.

Are you a mobile tester looking to learn something new? Are you a software tester, developer, product manager or completely new to mobile testing? Then you should read this book as it contains lots of insights about the challenging job of a mobile tester from a practical perspective.

1000 carefully designed C++ problems across 8 progressive volumes — from your very first line of code to advanced algorithms. Every single solution is compiled and tested on g++, with hints, complexity analysis, and a clear explanation for each. A complete, structured path from beginner to mastery.

It's never been easier to build an AI agent — and never been harder to make one that actually works. This book takes you from language model foundations to production-ready multi-agent systems with the depth to predict failure before it happens, engineer graceful degradation over catastrophic failure, and take absolute architectural ownership. Get the paperback from amazon.

Build a maintainable Rails application by using next-generation gems from the dry-rb and rom-rb suite. This book shows you how to easily bring those gems into the web framework you know and... well, the one you know.

Build production-grade RAG systems in C# — from an 80-line Hello World to a fully deployed Azure pipeline with the Microsoft Agent Framework, MCP, GraphRAG, multi-agent orchestration, eval gates, and EU AI Act-ready audit trails. 668 pages, 25 chapters, one evolving enterprise project, every line of code runnable in .NET 10.

Build a working SQL database engine in C++20 -- from raw pages to a realquery executor -- one compilable, measured challenge at a time. Everyperformance claim is a benchmark you run yourself; every unit ends with areal bug, caught red-handed and fixed. Nothing asserted. Everythingdemonstrated.

AI doesn’t fail loudly. It generates code that looks correct and compiles anyway. This book shows you how to make AI dependable by building the context and guardrails that keep your team shipping instead of debugging.

Strategy is the difference between making a wish and making it come true. To make your cloud journey a reality, and not just a wish, you’ll want to stay clear of buzzwords and product minutiae. Instead, focus on principles, decision models, and trade-offs that you can communicate broadly throughout your organization. This book tells you how. Paperback editionHardcover edition