Google Cloud Architect Handbook Designing highly available and resilient architectures on Google Cloud (Kumar, Ajay) (z-library.sk, 1lib.sk, z-lib.sk)

【One-Line Pitch】 A practical, hands-on guide to designing resilient, secure, and cost-aware systems on Google Cloud, written for developers, IT professionals, and students who want to move from cloud fundamentals to real architectural decisions and certification readiness. 【Book Arc】 - **Opening (~0%–15%)**: Frames cloud computing basics, GCP's value proposition, and the architect's role, then walks through getting started with the platform and its foundational services. - **Early (~15%–35%)**: Builds the core toolkit — compute and storage options (VMs, GKE, Anthos, Cloud Storage, Cloud SQL, AlloyDB, Bigtable, Spanner, Datastore), data and analytics (BigQuery, Dataproc, Dataflow, Dataplex, Data Fusion, Looker), and ML/AI services (Vertex AI, Document AI, Agent Builder). - **Middle (~35%–55%)**: Moves into architecture-critical domains: VPC networking, load balancing, DNS/CDN/NAT, hybrid connectivity, IAM roles and policies, service accounts, and organization-level governance. - **Late (~55%–80%)**: Covers operational and resilience concerns — DevOps and SRE practices (IaC, Deployment Manager, Workflows, Cloud Functions), high availability and disaster recovery (regions/zones, redundancy, replication, backup, DR tiers), and logging, monitoring, and troubleshooting. - **Ending (~80%–100%)**: Extends to hybrid and multi-cloud strategies (Cloud Interconnect, Anthos, multi-cloud security and orchestration) and closes with Professional Cloud Architect exam preparation, mock tests, and study resources. 【Key Takeaways】 - **Cloud fundamentals come first** (Opening): deployment models (public, private, hybrid) and service models (IaaS, PaaS, SaaS, serverless) are explained before any GCP-specific service, giving readers a mental model to place later topics. - **Compute and storage choices are architectural decisions** (Early): the book contrasts VMs, GKE, and Anthos alongside Cloud SQL, AlloyDB, Bigtable, Spanner, and Datastore, with guidance on choosing the right database rather than listing services in isolation. - **Data and AI are treated as first-class platform capabilities** (Early): BigQuery, Dataproc, Dataflow, Dataplex, Data Fusion, and Looker are covered with key features, pricing, and best practices, while Vertex AI, Document AI, and Agent Builder extend the platform into ML and generative AI. - **Networking and identity are the backbone of secure design** (Middle): VPC concepts, peering, shared VPC, firewall best practices, load balancing, Cloud Armor, and IAM roles/policies/service accounts are presented as the controls that make architectures both connected and governed. - **Resilience is designed, not assumed** (Late): high availability and DR are broken into redundancy, fault tolerance, dependency management, replication, backup, and cold/warm/hot DR strategies, with testing and simulation as explicit steps. - **Observability closes the operational loop** (Late): Cloud Logging, Cloud Monitoring, uptime checks, and Cloud Trace are framed as the tools for detecting and resolving issues, supported by an e-commerce optimization case study. - **Hybrid and multi-cloud are strategic, not edge cases** (Ending): Cloud Interconnect, Anthos, and multi-cloud security/automation are covered with case studies, reflecting real enterprise constraints. - **Certification readiness is built in** (Ending): each chapter includes multiple-choice questions, case studies, and answer keys, and the final chapter maps directly to the Professional Cloud Architect exam objectives. 【Reading Tips】 - **Skim the opening chapters if you already know cloud basics**, but read the service-model comparison carefully — it anchors later trade-off discussions. - **Deep-read the compute/storage and database selection sections**, since choosing the right service is where most architectural mistakes happen. - **Treat networking and IAM as high-attention zones**: these chapters are dense with controls and are the most likely to appear in both real designs and exam scenarios. - **Use the case studies and MCQs as checkpoints** after each chapter rather than saving them for the end; they reveal gaps in understanding early. - **Pair the HA/DR and monitoring chapters together** — resilience without observability is incomplete, and the book treats them as complementary. 【Coverage Limits】 This guide is based on stratified excerpts covering the table of contents, preface, and selected chapter material; detailed technical content, code snippets, and case-study specifics are only partially represented, so some chapter-level depth is not fully captured here.