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Zustand: New.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: As New. Unread book in perfect condition.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: As New. Unread book in perfect condition.

Paperback. Zustand: new. Paperback. Build faster, safer, and more capable SQL Server systems by mastering modern T-SQL from core querying through AI-ready vector search.Many developers can write queries that work, but struggle when data grows, workloads mix OLTP and reporting, and new requirements like JSON, regex text processing, and semantic search arrive. The result is fragile code, slow performance, and features that are hard to ship confidently.This guide helps you move past that ceiling. You will learn how SQL Server executes T-SQL, how to model real systems, and how to write production queries that stay correct and predictable under pressure, including native vector search workflows for AI applications.select filter sort and paginate results with clear intent and stable performancewrite reliable joins subqueries and apply patterns for real retrieval problemsuse grouping aggregation and window functions for analytics and reportingchoose data types that protect correctness and improve execution plansbuild expressions and conditional logic that stay readable and sargablework confidently with dates times and time series functionsdesign stored procedures and user defined functions for reusable logiccontrol transactions isolation levels and concurrency in multi user systemshandle errors with try catch throw and xact safe patternsread execution plans design indexes and use query store to spot regressionsapply intelligent query processing and handle parameter sensitive plansmodel schemas that survive growth including partitioning and archival strategiesclean parse and validate text with string functions and native regex supportstore vectors run exact and approximate similarity search and index embeddingsdesign embedding tables that connect semantic results to business dataprepare chunked data implement semantic search and build rag pipelines with external modelssecure databases with authentication authorization row level security and auditingprotect data with encryption backup restore and high availability planningmanage the database lifecycle with dacpacs testing and ci cd for sql server and azure sqlavoid common query and schema anti patterns and ship with a production checklistThroughout the book you will find working T-SQL patterns and scripts that you can adapt directly into your own projects, from day to day querying to vector based semantic search.Grab your copy today and start writing T-SQL that holds up in production and supports modern AI features. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Zustand: new. Paperback. Design Elasticsearch 9 vector search and RAG systems that stay fast, accurate, and predictable in production.Elasticsearch 9 changes how vector search, quantization, and hybrid retrieval behave under real load, yet many teams still ship clusters that fall over when traffic or data grows. Guesswork around HNSW settings, BBQ, DiskBBQ, and filtered ANN often leads to fragile systems and painful outages.This book walks you through the full lifecycle of Elasticsearch 9 search workloads, from upgrade planning and data modeling to dense vectors, BBQ and DiskBBQ, ESQL workflows, and production playbooks, so you can reason about behavior instead of tuning by accident.Understand Elasticsearch 9 search architecture, shards, segments, and upgrade paths for vector heavy clustersModel documents and chunks for hybrid retrieval and RAG with clean metadata for filters multi tenant access and citationsChoose and tune dense_vector mappings similarity functions and HNSW parameters for balanced recall and latencyApply Better Binary Quantization and DiskBBQ to cut memory and storage while keeping quality with oversampling and rescoringDesign filtered vector search that actually works using ACORN concepts and patterns for ACL and time sliced dataBuild maintainable hybrid search that combines lexical search vectors RRF fusion and rerankers without unreadable queriesUse retrievers as the primary query interface and wire them into ESQL FORK and FUSE pipelinesMap and query semantic_text fields and roll out semantic retrieval safely across models and indicesIntegrate inference endpoints for embeddings and reranking with clear security observability and fallback pathsAdopt ESQL LOOKUP JOIN for in cluster enrichment and cleaner joins between chunk and source indicesRun relevance experiments, Rally style benchmarks, and capacity planning focused on recall latency and cost per queryFollow concrete production playbooks and reference implementations for hybrid retrieval, RAG services, and ESQL based search stacksYou also get practical add ons, including deployment checklists, reference pipelines using retrievers, rerankers, and ESQL LOOKUP JOIN, plus a benchmark harness with Rally style tests and a capacity sizing worksheet that you can adapt to your own environment.Throughout the chapters you work through realistic JSON mappings, curl examples, Docker and configuration snippets, and ESQL queries that you can lift into your own clusters with minimal adjustment.Grab your copy today and build Elasticsearch 9 search systems you can trust in production. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Zustand: new. Paperback. Build faster, safer, and more capable SQL Server systems by mastering modern T-SQL from core querying through AI-ready vector search.Many developers can write queries that work, but struggle when data grows, workloads mix OLTP and reporting, and new requirements like JSON, regex text processing, and semantic search arrive. The result is fragile code, slow performance, and features that are hard to ship confidently.This guide helps you move past that ceiling. You will learn how SQL Server executes T-SQL, how to model real systems, and how to write production queries that stay correct and predictable under pressure, including native vector search workflows for AI applications.select filter sort and paginate results with clear intent and stable performancewrite reliable joins subqueries and apply patterns for real retrieval problemsuse grouping aggregation and window functions for analytics and reportingchoose data types that protect correctness and improve execution plansbuild expressions and conditional logic that stay readable and sargablework confidently with dates times and time series functionsdesign stored procedures and user defined functions for reusable logiccontrol transactions isolation levels and concurrency in multi user systemshandle errors with try catch throw and xact safe patternsread execution plans design indexes and use query store to spot regressionsapply intelligent query processing and handle parameter sensitive plansmodel schemas that survive growth including partitioning and archival strategiesclean parse and validate text with string functions and native regex supportstore vectors run exact and approximate similarity search and index embeddingsdesign embedding tables that connect semantic results to business dataprepare chunked data implement semantic search and build rag pipelines with external modelssecure databases with authentication authorization row level security and auditingprotect data with encryption backup restore and high availability planningmanage the database lifecycle with dacpacs testing and ci cd for sql server and azure sqlavoid common query and schema anti patterns and ship with a production checklistThroughout the book you will find working T-SQL patterns and scripts that you can adapt directly into your own projects, from day to day querying to vector based semantic search.Grab your copy today and start writing T-SQL that holds up in production and supports modern AI features. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Zustand: new. Paperback. Design Elasticsearch 9 vector search and RAG systems that stay fast, accurate, and predictable in production.Elasticsearch 9 changes how vector search, quantization, and hybrid retrieval behave under real load, yet many teams still ship clusters that fall over when traffic or data grows. Guesswork around HNSW settings, BBQ, DiskBBQ, and filtered ANN often leads to fragile systems and painful outages.This book walks you through the full lifecycle of Elasticsearch 9 search workloads, from upgrade planning and data modeling to dense vectors, BBQ and DiskBBQ, ESQL workflows, and production playbooks, so you can reason about behavior instead of tuning by accident.Understand Elasticsearch 9 search architecture, shards, segments, and upgrade paths for vector heavy clustersModel documents and chunks for hybrid retrieval and RAG with clean metadata for filters multi tenant access and citationsChoose and tune dense_vector mappings similarity functions and HNSW parameters for balanced recall and latencyApply Better Binary Quantization and DiskBBQ to cut memory and storage while keeping quality with oversampling and rescoringDesign filtered vector search that actually works using ACORN concepts and patterns for ACL and time sliced dataBuild maintainable hybrid search that combines lexical search vectors RRF fusion and rerankers without unreadable queriesUse retrievers as the primary query interface and wire them into ESQL FORK and FUSE pipelinesMap and query semantic_text fields and roll out semantic retrieval safely across models and indicesIntegrate inference endpoints for embeddings and reranking with clear security observability and fallback pathsAdopt ESQL LOOKUP JOIN for in cluster enrichment and cleaner joins between chunk and source indicesRun relevance experiments, Rally style benchmarks, and capacity planning focused on recall latency and cost per queryFollow concrete production playbooks and reference implementations for hybrid retrieval, RAG services, and ESQL based search stacksYou also get practical add ons, including deployment checklists, reference pipelines using retrievers, rerankers, and ESQL LOOKUP JOIN, plus a benchmark harness with Rally style tests and a capacity sizing worksheet that you can adapt to your own environment.Throughout the chapters you work through realistic JSON mappings, curl examples, Docker and configuration snippets, and ESQL queries that you can lift into your own clusters with minimal adjustment.Grab your copy today and build Elasticsearch 9 search systems you can trust in production. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Zustand: new. Paperback. Migrate your UWP, WPF, and WinForms apps to WinUI 3 with a clear, end to end plan that you can actually ship.Moving a real production desktop app to WinUI 3 and the Windows App SDK is not a simple control swap. You are changing the app model, activation flow, windowing behavior, deployment story, and often your architecture at the same time.Migrating to WinUI 3: Modernize Legacy Windows Desktop Applications guides you through that change step by step, from fundamentals and solution architecture to platform features, Fluent design, deployment, testing, and release discipline, so you can modernize without breaking what users rely on.Understand what WinUI 3 is, how it differs from the UWP app model, and how the Windows App SDK runtime and versioning strategy affect your app.Choose the right migration approach for your product, rewrite vs incremental vs hybrid, backed by a practical readiness checklist and risk register.Design a solution architecture that separates UI, business logic, and infrastructure, using shared class libraries, multi targeting, dependency injection, configuration, and logging.Set up WinUI 3 projects the professional way, with clean App startup, window creation patterns, threading and DispatcherQueue usage, windowing with Window and AppWindow, and resilient navigation.Handle activation and single instance behavior using AppInstance and ActivatedEventArgs, including file and protocol activation that routes into your existing navigation stack.Migrate UWP features confidently, replacing lifecycle assumptions, mapping APIs such as Window.Current, CoreDispatcher, and CoreWindow, updating XAML namespaces and styles, and moving notifications, background tasks, and localization to Windows App SDK and MRT Core.Modernize WPF and WinForms apps with a feature adoption first mindset, safely calling Windows App SDK feature APIs, hosting WinUI content with islands concepts, and building hybrid shells that keep legacy modules behind clean interfaces.Apply Fluent design to real desktop layouts with themes, typography, light and dark mode, Mica and Acrylic backdrops with SystemBackdrop, and strong accessibility support for high contrast and keyboard users.Make deliberate packaging and deployment choices, packaged vs unpackaged vs external location, framework dependent vs self contained, using the bootstrapper API, dynamic dependencies, and a diagnostics friendly deployment troubleshooting approach.Strengthen testing and performance with golden path regression strategies, automated UI testing options, performance profiling for startup, memory, and responsiveness, plus stability hardening, crash triage, telemetry basics, and a release playbook with rollback and post migration maintenance.This is a code heavy guide, with practical C# and XAML snippets that show how to apply WinUI 3 and Windows App SDK patterns in real project scenarios, not just in isolated samples.If you are responsible for shipping a modern WinUI 3 desktop app from a legacy UWP, WPF, or WinForms codebase, grab your copy today. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.