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Zustand: As New. Unread book in perfect condition.

Taschenbuch. Zustand: Neu. Neuware - Build a semantic layer that gives BI tools, LLMs, and data agents the same governed business meaning.Giving an AI model access to a warehouse does not guarantee analytically correct answers. Valid SQL can still use the wrong metric, join at the wrong grain, duplicate revenue, misinterpret time, expose restricted data, or produce a different answer from the company dashboard.This practical guide shows how to move stable business logic out of prompts and ad hoc queries and into governed semantic models. You will learn how to design metrics that remain consistent across traditional analytics and AI systems, then implement and operate them with dbt MetricFlow, Cube Cloud, and Snowflake Semantic Views.- Model grain, entities, primary keys, joins, facts, dimensions, measures, and semantic contracts correctly- Prevent fanout, chasm traps, duplicate aggregation, unsafe distinct counts, and ratio errors- Build simple, ratio, derived, cumulative, and conversion metrics with governed time semantics- Implement semantic models with MetricFlow, Cube Cloud, and Snowflake Semantic Views- Prepare metrics for LLMs with descriptions, synonyms, AI context, verified queries, literal resolution, and domain routing- Connect semantic services to applications and agents through SQL, REST, GraphQL, SDKs, and MCP- Design governed MCP tools for metric discovery and semantic querying instead of unrestricted raw SQL- Combine structured analytics with document retrieval, search, and multi-step agent reasoning- Protect AI analytics with RBAC, row security, masking, tenant isolation, identity propagation, and least privilege- Defend against prompt injection, tool misuse, semantic model poisoning, data exfiltration, and unsafe write actions- Test metric behavior with semantic unit tests, paraphrase evaluations, regression suites, and cross-engine contract tests- Control caching, pre-aggregations, materializations, query fanout, latency, concurrency, and agent cost- Govern metric ownership, certification, versioning, deprecation, drift detection, and multi-platform synchronizationThe guide includes extensive SQL, YAML, Python, Shell, JSON, and GraphQL examples that connect semantic concepts to realistic implementation, testing, security, agent integration, and production operations.You will also learn how to build a production semantic trust chain so that transformations, metric definitions, execution engines, access policies, BI tools, and AI agents stay aligned around the same approved business definitions.Grab your copy today and build analytics systems where AI flexibility works on top of governed, testable, and consistent business semantics. …

Taschenbuch. Zustand: Neu. Neuware - Build a private, resilient smart home that keeps working locally across Matter, Thread, Zigbee, Wi-Fi, and Ethernet.Modern smart homes can become fragile when devices depend on cloud services, poorly planned radio networks, or confusing combinations of hubs, bridges, controllers, and protocols. This practical guide shows you how to design Home Assistant as a dependable local control system instead of a collection of disconnected devices.You will learn how the technologies actually fit together, how to choose the right hardware and integration path, and how to troubleshoot failures from the radio layer through IPv6, multicast discovery, Matter fabrics, and Home Assistant automations.- Choose between a Raspberry Pi 5 and x86-64 mini PC and build a reliable Home Assistant host with proper storage, cooling, power, Ethernet, and backups- Design a strong Zigbee mesh with coordinators, routers, parent relationships, channel planning, direct binding, groups, OTA updates, and coordinator migration- Deploy ZHA or Zigbee2MQTT with MQTT while understanding the tradeoffs between each architecture- Run OpenThread Border Router with a dedicated Thread radio and manage Operational Datasets, credentials, preferred networks, and multiple Border Routers- Commission Matter devices over Wi-Fi, Ethernet, and Thread while understanding QR setup, Bluetooth, PASE, CASE, fabrics, access control, and multi-admin sharing- Compare direct Zigbee integration, native vendor integrations, and Matter bridges without assuming the newest option always exposes the most features- Plan IPv6, mDNS, multicast, VLANs, firewalls, Docker networking, and 2.4 GHz coexistence so local discovery and routing remain reliable- Build mixed-protocol automations for lighting, occupancy, climate control, leak protection, locks, sensors, and other local devices- Use dedicated Zigbee and Thread radios instead of unreliable shared-radio shortcuts and diagnose USB interference, weak links, and power problems- Test internet outages, Home Assistant failures, radio loss, Border Router failover, backups, restores, and hardware migration before a real failure occursThe guide includes practical YAML, Shell, MQTT, Docker, and configuration examples that help you turn the architecture into a functioning local system and diagnose problems at the layer where they actually occur.Build a smart home you can understand, maintain, recover, and keep under local control. Grab your copy today. …

Paperback. Zustand: new. Paperback. Ship fast, private, and efficient AI in the browser with ONNX Runtime Web, WebGPU, and a proven pipeline from training to production.Developers face real constraints in the browser, from cross origin isolation and CSP to GPU limits, storage quotas, and variable device performance. This book gives you a practical end to end system that handles those constraints while delivering low latency features users can trust.You will learn when client side inference is the right call, how to export and optimize models, and how to run them reliably with WebGPU and WASM using clean fallbacks. The result is a codebase that is faster to maintain, easier to ship, and ready for production.Decide where inference should run using clear cost, privacy, and latency trade offsExport PyTorch models to ONNX with external data to handle 2 GiB limitsConvert and optimize graphs into ORT format and apply mixed precision fp16 safelyUse ONNX Runtime Web SessionOptions, IO binding, and device tensors to keep data on GPUApply graph capture on WebGPU for static shapes and plan around binding size limitsReach stable CPU performance with WASM threads and SIMD through cross origin isolationProbe WebGPU features including shader f16, subgroups, and timestamp queriesSelect providers with a backend matrix that adapts across WebGPU, WebNN, and WASMBuild tokenizer workflows and pipelines with Transformers.js using device webgpuImplement preprocessing and postprocessing for images and audio with codecs and batchingCache large weights in IndexedDB and OPFS with quota checks and eviction handlingVersion and validate assets using manifests, ETags, and integrity checksStream and lazy load sharded weights with HTTP Range for faster first useHandle large models with KV cache tiling and binding size aware layoutsPartition graphs between WebGPU and WASM for selective fallbacks on weaker devicesBuild real projects end to end, including Stable Diffusion Turbo with fp16 weightsWire Whisper Tiny for streaming capture with VAD for robust speech inputShip real time background removal with camera compositing for the webDeliver CLIP image search with local embeddings and an IndexedDB vector indexSet headers and CSP correctly, avoid COEP credentialless pitfalls, and keep isolationProfile performance with ORT logs and the WebGPU inspector to remove bottlenecksMigrate cleanly from ONNX.js and TensorFlow.js to ORT Web without breaking flowsStand up production telemetry, error boundaries, and alerting that respect privacyPlan costs for CDN egress, caching, and storage, with practical distribution strategiesFuture proof with feature detection for WebNN and NPUs and a maintenance roadmapThis is a code heavy guide, with working examples that show IO binding, fp16 kernels, manifests, service workers, feature probes, and complete project wiring so you can ship real products.Get the guide that turns browser AI from a demo into a dependable product, grab your copy today. 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 a reliable multi engine lakehouse catalog with Apache Polaris and keep Iceberg, Delta and your engines under control.Modern data platforms rarely run on a single engine. Spark, Trino, Flink, managed warehouses and streaming jobs all compete for the same data in object storage. Without a shared catalog, schemas drift, permissions fragment and simple changes become risky and slow.Build Multi-Engine Lakehouse Catalogs with Apache Polaris gives data platform engineers, architects and senior data engineers a practical way to standardize on an open catalog service. You will learn how to design, deploy and operate Polaris so it can coordinate Iceberg and Delta tables, enforce governance and integrate cleanly with your existing engines and infrastructure.Understand the catalog problem in multi engine lakehouses and why open catalog services matterCompare Iceberg, Delta, Hudi, Paimon and other table formats from a catalog perspectivePosition Polaris among Hive Metastore, Glue, Unity Catalog, Nessie and similar servicesModel catalogs, namespaces, tables, views and securable objects for real data productsConfigure relational metastores, JDBC pools, retries and capacity planning for high write concurrencyDesign RBAC policies, roles and namespaces that reflect producer and consumer responsibilitiesAuthenticate users, services and engines, and implement credential vending for secure object storage accessFederate existing Hive metastores and external REST catalogs, and govern native and federated data consistentlyRegister and query Delta Lake and other generic tables through Polaris alongside IcebergConnect Spark, Trino and managed engines to Polaris using open catalog servicesBuild streaming and CDC pipelines with Kafka and Flink that write into Iceberg tables managed by PolarisDeploy Polaris on Kubernetes using Helm, TLS, network policies and high availability patternsSet up logging, metrics, tracing and SLOs for catalog reliability and on call operationsUse migration playbooks from Hive and vendor catalogs, including phased rollout, cutover and rollback strategiesDesign multi tenant and compliance focused catalogs, with isolation and sharing patterns that scaleHarden Polaris against storage, network and metastore failures, and run chaos experiments and recovery drillsApply reference architectures for analytics, streaming and hybrid managed or self hosted engines, and avoid common anti patternsFollow a pragmatic roadmap and implementation checklist to structure your first Polaris adoption projectThe book includes concrete migration playbooks, reference architectures and an implementation checklist so you can move from theory to a staged rollout that fits your platform and governance constraints.Throughout the chapters you will work with realistic configuration snippets and code examples for Spark catalogs, Trino connectors, streaming jobs and Kubernetes deployments, so you can adapt the patterns directly into your own repositories and environments.Grab your copy today and design a multi engine lakehouse catalog your data platform can trust. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Zustand: New. Print on Demand.

Zustand: New. Print on Demand.

Zustand: New. Print on Demand.

PAP. Zustand: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Zustand: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Zustand: New. Print on Demand.

Zustand: New. Print on Demand.

PAP. Zustand: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Zustand: new. Paperback. Build reliable internal tools faster with a spreadsheet UI, secure access, and production-ready serverless workflows.Teams struggle to ship admin panels that operators trust, keep costs predictable, and pass security reviews. Custom dashboards take time, generic no-code tools rarely fit policy, and ad hoc scripts become a maintenance burden.This book shows a practical path with Rowy on Firestore. You will set up a clean stack, lock it down with Firebase Auth, App Check, and Security Rules, and add business logic with Rowy Run and Cloud Functions v2. Every step is grounded in realistic workflows, from content approvals to back office queues with SLAs.provision firebase projects, enable firestore and auth, and deploy rowy and rowy run on cloud rundesign collections, subcollections, and denormalized models that scale without hot partitionsconfigure providers, blocking functions, app check, and role based access with custom claimswrite security rules that enforce per field and per role policies for safe reads and writesadd derivative fields, action buttons, callable functions, and webhooks for real side effectsbuild rowy tables with field types, connected references, and algolia backed viewsimport and export data safely with idempotent ids and guarded reimportsuse query insights and key visualizer to find slow queries and fix index shapesrun the emulator suite in ci with rules unit tests and function testscontrol costs with batch writes, listener hygiene, and index pruningship admin workflows, including content catalog approvals and publish to cdnrun back office queues with ticket ownership, escalation, notifications, and sla mathstream audit events to bigquery and use cloud audit logs for forensicspractice recovery with backups, clones, and point in time restore drillssupport multi tenant setups with identity platform and clear data boundariesThis is a code heavy guide. You get working snippets for rules, functions v2, rowy run actions, backup scripts, terraform baselines, ci workflows, and bigquery pipelines so you can implement real projects, not mock examples.Grab your copy today and ship internal tools your operators can rely on. 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. Run Kubernetes on immutable Talos Linux with repeatable GitOps, reliable upgrades, and calm incident playbooks.Running clusters at scale is hard when nodes drift, access is ad hoc, and upgrades break busy weeks. Talos Linux removes the shell and gives you a single, authenticated API for every operational task, from bootstrap to disaster recovery.This book shows a complete production model, including networking, storage, security, GitOps, upgrades, observability, multi cluster control, and tested recovery runbooks, so platform teams can ship confidently.Operate Talos the right way, API only operations with no SSH, roles for os admin os operator os reader, clear RBAC and audit trailsUnderstand apid machined trustd, request flow on a node, discovery service, and resilient access with kubeprismProvision at scale with cloud images, PXE and Image Factory, plus Omni and SideroLink for bare metalDefine machine configuration as code, overlays per environment, live patch and reboot strategies, drift detection, CI validation and policy checksBuild reliable networking, Cilium or Calico on Talos, kube proxy replacement choices, KubeSpan planning, MTU math, and local API fallback patternsChoose storage with intent, Rook Ceph device prep and failure domains, Longhorn guardrails and recovery, local PV strategies with TopoLVM or MayastorApply a practical security baseline, Secure Boot, TPM backed disk encryption for state and ephemeral, Talos API RBAC, client rotation, scoped automationEnforce image provenance, cosign verification, and admission policies aligned to platform controlsRun GitOps end to end, repo layout for machine configs and platform add ons, SOPS with age, Flux bootstrap and reconciliation orderUpgrade with confidence, Talos and Kubernetes sequencing, dry runs, rollbacks, blue green and canary clusters, disruption budgets and surge drain tacticsObserve and fix fast, Talos logs and dmesg, support bundles, concrete control plane health checks for etcd API server schedulerTroubleshoot networks with CNI checks, KubeSpan verification, and packet level tools that work in real outagesManage fleets, Omni templates and machine classes with autoscaling, Cluster API provider Talos with a pinned provider matrix, migration patterns, shared registry mirrors and policy baselinesRecover predictably, etcd snapshot strategy with storage and retention, full control plane restore, break glass access with kubeprism and talosctl, and air gapped bootstrap with mirrors and pre seedingThis book is a code heavy guide with working Bash YAML JSON and Kubernetes manifests that you can adapt directly in real clusters.Get the operating model your platform deserves, and start running Talos Linux in production with confidence today. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Zustand: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Zustand: New. Print on Demand.

PAP. Zustand: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Zustand: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.