Databases

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.

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.

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.

This book is for anyone who wants to feel more confident. Over eleven chapters, we put together a single sample application, and we watch it grow from a simple table used by one employee into a more complex system that's divided into different parts, has backups, is secure using OAuth, and is audited.

Most analytics books teach tools first and thinking later. Data Analytics Foundations reverses that order — building the judgment, technical skills, and communication discipline required to transform raw data into trusted decisions. From spreadsheets and SQL to data quality, visualization, and analytical reasoning, this book equips readers with a complete foundation for professional analytical practice.

Most data modelling books teach you the craft. This one teaches you the reality. Seven chapters of honest, practical guidance from real project experience — covering conceptual modelling, governance, enterprise challenges, and the human side of data work that nobody else writes about.

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.

Most developers treat Hibernate as a black box that turns objects into rows. This book opens the box, shows you exactly what happens inside, and teaches you how to make it work for you at scale.