Books

Certainty feels safe - but in an uncertain world, it's a bug, not a feature. A Data Engineer shows how the craving for absolute truth is a form of "overfitting," and how thinking in probabilities makes you more creative, resilient, and right more often.

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.

PySpark from page one. Ten chapters that take a Python user who knows pandas and turn them into someone who can write, read, and debug production PySpark, without a three-chapter detour through distributed-computing theory.

AWS replaced Amazon Q with Kiro — an AI IDE that writes specs before code. 14 chapters covering spec-driven development, AI agents, hooks, MCP integrations, AWS deployment, and the honest comparison with Cursor and Copilot. The definitive guide.

You passed the Java course. Then you opened a real codebase and nothing looked like the slides. Car extends Vehicle and Box<T> got you through the exam. They also taught you habits you now have to unlearn. Java for the Real World re-teaches the syllabus one concept at a time, with code people actually ship: payment gateways, message types, generic repositories, retrying HTTP clients. Each chapter shows the toy you were taught, names why it misled you, and gives you the version a senior would keep.

The tech job market changed. Entry-level dropped 73%, AI screens resumes, interviews test system design. This is the no-BS guide: portfolio, resume, LinkedIn, interviews, negotiation, and your first 90 days. Written by someone who hires engineers.

O abordare vizuală și practică a programării C/C++, bazată pe ani de experiență în predare. Cartea combină teoria, exemplele și schițele inspirate din caietele reale ale cursanților pentru a facilita înțelegerea conceptelor complexe.

Why your model means nothing if no one acts on itThe gap no one talks about. 

An enterprise-focused guide to building a safe, governed AI SRE agent skill that reviews Kubernetes platforms on Talos Linux. Read-only by default, auditable, and grounded in evidence rather than guesswork.

🌙 This guide will help you discover: ✨ when love will come into your life✨ why your past relationships turned out the way they did✨ what kind of partner is truly right for you✨ how to break out of painful patterns✨ how to understand your romantic destiny through astrology

Kubernetes exposes plenty of state, but operators still have to reconstruct operational meaning from scattered Pods, Services, EndpointSlices, Events, PVCs, owner references, and status fields. This ebook uses `kctx`, a small read-only Kubernetes context engine, to show how deterministic entities, relations, signals, graphs, namespace snapshots, CRD adapters, and stable JSON contracts can turn raw cluster data into reusable context for humans, tools, and AI agents. It is written for SREs, platform engineers, Kubernetes operators, infrastructure developers, and AI tooling builders who want better primitives than raw YAML and improvised troubleshooting pipelines.

Software and infrastructure only create value when they pull in the same direction as the business. This book shows IT leaders how to get there: from shaping IT strategy and the architecture roadmap to the daily discipline of IT and architecture governance, all built on a pattern-based approach that adapts to your organization rather than forcing it into a template.Grounded in established frameworks such as TOGAF, COBIT, and ITIL, it pairs solid fundamentals with numerous real-world examples — and gives growing weight to compliance and IT security, now central concerns of any IT management agenda.This English edition is based on the German standard work on the subject, fully revised in its 4th edition (late 2024). It is current with TOGAF 10 and reflects recent developments in business-oriented enterprise architecture — including the open-source tool EDGY and patterns for digital strategy — alongside the latest trends in IT risk management and cybersecurity architecture.

A child bride. A brilliant poet husband. A marriage built on silence. The Silent Lotus is a haunting tale of duty, loneliness, and quiet resilience in colonial-era India. Will the lotus ever rise above the murky waters?