PowerShell for Senior Engineers: Platform Architecture, AI Pipelines, Large-Scale Data Engineering, and Cross-Platform Fleet Automation (PowerShell Pro Series) by Diego S
English | March 19, 2026 | ISBN: N/A | ASIN: B0GT3VKBZH | 737 pages | EPUB | 1.42 Mb
This book teaches you how to build the platforms, pipelines, and systems that entire organizations rely on. This volume covers the full senior-level scope: enterprise platform architecture, integration with LLMs and AI agents, large-scale data engineering, Linux and macOS fleet management, and supply chain security-all with production-ready code and complete solutions in every chapter.
What sets this book apart:Architecture, not just scripting - each chapter addresses real-world design decisions: when to use Event Grid vs. Service Bus, how to model SLOs, how to build a memory-based LLM agent with security containment, how to structure a data lake into zones.AI and LLMs from PowerShell - integration with Azure OpenAI, MLOps pipelines, autonomous agents using the ReAct pattern, multi-agent systems with persistent memory, and full observability. No dependencies on third-party SDKs: everything from pure PowerShell.Three enterprise capstone projects - an autonomous self-healing platform, a zero-trust fleet migration, and a comprehensive supply chain observatory.30 graded exercises with complete solutions - Intermediate, Advanced, and Senior levels, building real-world tools you can deploy directly to your environment.80 self-assessment questions.12 chapters. 12 senior-level skills:Chapter 1 - Platform Architecture: REST APIs with Pode, multi-tenant automation, governance guardrails, platform versioning, and backward compatibilityChap. 2 - Event-Driven Architecture: Azure Event Grid, Service Bus pub/sub, Azure Functions serverless, Durable Functions, dead-letter queues, and idempotenceChap. 3 - Resilience and Reliability: Bulkhead and throttle patterns, chaos engineering with Azure Chaos Studio, health modeling, SLOs/SLAs/SLIs, AIOps runbooks, and distributed tracingChap. 4 - Azure OpenAI and LLMs: Chat completions, structured JSON output, function calling, embeddings and semantic search, streaming, and cost controlChapter 5 - AI Pipelines and MLOps: Azure ML, training job orchestration, model evaluation and promotion gates, drift detection, chained prompt pipelinesChapter 6 - AI Agents: ReAct Pattern, single-purpose and multi-agent agents, agent memory, security containment, and observability in productionChapter 7 - Enterprise ETL: Secure extraction patterns, schema validation, idempotent loads, orchestration with dependency graphs, CDC, and data quality frameworksChapter 8 - Synapse, Data Lake, and Fabric: Synapse Analytics automation, serverless SQL, Delta Lake on ADLS, Microsoft Fabric, and cost governanceChapter 9 - Cross-Platform Fleet Management: Linux and macOS at scale, SSH with ProxyJump, configuration drift detection, heterogeneous package management, certificate lifecycle, and CIS complianceChapter 10 - DevSecOps: Vulnerability scanning (Trivy, Checkov), SBOM with Syft, secret scanning, automated rotation, artifact signing with Cosign, and SLSA provenanceChapter 11 - Capstone Projects: Autonomous platform self-healing, fleet migration to zero-trust, comprehensive supply chain observatoryChapter 12 - Senior Refere
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