CQRS in Practice is a hands-on guide to designing scalable, maintainable software with Command Query Responsibility Segregation. Through practical examples and real-world insights, you'll learn when CQRS is the right choice, how to implement it effectively, and how to avoid the pitfalls of overly complex architectures.
Two builds, one schema, zero faith required. From Table to Twin walks an industrial digital twin's SQL Server and EF Core backbone by hand and from code, then prints exactly where Database First and Code First disagree — for .NET developers who check every table against a live, running console.
One entity. One mapping. Clean context. From Entity to Context shows how to organize enterprise EF Core Code First projects with configuration classes, Fluent API, migrations, indexes, relationships, and a clean DbContext across the NEXUS-1 digital-twin schema.
One database. Seventeen sectors. Fully keyed. From Schema to System maps the complete SQL Server / EF Core data backbone behind the NEXUS-1 digital twin, showing how telemetry, alarms, root-cause analysis, reinforcement learning, compliance, reporting, and audit become one queryable system.
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My book is out. Not a SQL tutorial. Not a beginner's guide to databases. It's for the person who writes queries every day and still wonders — why did that take 4 seconds? Why did those two records overwrite each other? Why did the system collapse under load when everything looked fine? My Database as a Developer. 8 chapters. Real answers.
Unlock the power of data with Python. Learn how to clean, analyze, visualize, and model real-world data using NumPy, Pandas, SQL, and machine learning techniques.
A practical 62-page manual to master SQL for data analysis from scratch. Covers SELECT, JOINs, CTEs, Window Functions, and a complete RFM final project. Includes exercises, cheatsheets, and a real dataset.
This book is all about having a deep understanding of every configuration decision, every index design, every backup policy, and every failover drill. All the recipes in this book are for real-life tasks that you'd actually do, with all the exact commands and configurations that work on a production linux server running MySQL.
Structured Streaming, MLlib, GraphFrames, performance tuning, testing and CI, and the lakehouse. Eleven chapters that take a competent PySpark user from "the job runs" to "the on-call team trusts the job.
A practical guide to Cosmos DB for .NET developers who know relational databases and want to understand document thinking — not just the SDK. Built from a real production application, not documentation copy-paste.
Analytics Patterns for Intelligent Decision Systems shows how to move beyond dashboards into analytics systems that explain, predict, recommend, optimize, and improve business outcomes.Packed with SQL patterns, KPI frameworks, forecasting models, recommendation logic, and decision intelligence templates, this book gives data professionals a practical blueprint for modern analytics.Build systems that do more than report the past — build systems that help decide what should happen next.
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.