Books

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.

Become SnowPro Core certified in Snowflake!

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.

Master distributed systems through visual diagrams — from clock drift and CAP to Paxos, Raft, and distributed transactions, explained with clear illustrations instead of dense academic papers.

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.

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.

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.

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.