AI-Powered Development for Data, Frontend, Mobile & DevOps — Claude Code & Cursor: E2E Application Development Series 02

Level: Intermediate / Advanced
Duration: 4 Days / 16 Hrs (4 Hrs/Day)
Delivery: 20% Lecture / 80% Hands-On Labs
Domain Project: Supply Chain Management (SCM) Platform — Frontend, Data & Ops Track

Course Introduction

This program takes the SCM backend APIs and builds everything on top of them — starting with Figma design-to-code using Cursor, through a React/Next.js web app, a React Native mobile app, a Microsoft Fabric Ontology-powered data pipeline with analytics, a comprehensive AI-generated test suite, and a fully automated CI/CD pipeline with production monitoring. Cursor is the primary tool for design and frontend; Claude Code drives data engineering, testing, and DevOps generation.

Course Objective

  • Use Figma’s MCP integration with Cursor to generate production React components directly from design frames
  • Build production-ready React/Next.js web applications using Cursor’s AI-assisted workflow
  • Develop cross-platform React Native mobile apps with offline-first architecture using AI
  • Design and implement data ingestion, transformation, and analytics pipelines with Claude Code
  • Build a Microsoft Fabric Ontology semantic layer for the SCM domain (Entity Types, Properties, Relationships, Data Bindings)
  • Query ontology-bound SCM data using natural language (NL2Ontology)
  • Generate comprehensive E2E, performance, and security test suites using AI
  • Build complete CI/CD pipelines using GitHub Actions generated by Claude Code
  • Set up production observability: Prometheus, Grafana, ELK Stack, distributed tracing
  • Use Claude for AI-assisted log analysis, incident response, and runbook generation

Key Takeaways

  • A complete web and mobile front-end built on Figma-to-code workflows
  • A working Microsoft Fabric Ontology semantic layer with natural language querying
  • A production-grade CI/CD pipeline with monitoring, logging, and tracing in place
  • Hands-on AI-assisted incident response and documentation generation

Who Should Attend

  • Frontend and mobile developers adopting AI-assisted workflows
  • Data engineers building analytics and semantic layers
  • DevOps and platform engineers driving CI/CD and observability

Prerequisites

  • AI-Powered Backend Development (Series 01) or equivalent backend API experience
  • Working knowledge of React and basic CI/CD concepts

Course Outline

Day 1 — Foundations, Figma Design-to-Code & Web Development

  • Foundations Recap & Cursor Workflow — AI tool selection, component-first development
  • Figma → Cursor Design-to-Code — Figma MCP plugin workflow, design tokens, component variants
  • Web Application Development — Next.js App Router, state management, React Query, Zod validation
  • Labs: Cursor setup, design-to-code component generation (Button, DataTable, ProductCard, OrderStatusBadge), Inventory Dashboard, Order Management Interface, Supplier Portal, state management & testing

Day 2 — Mobile Development & Data Pipelines

  • Mobile App Development with Cursor — React Native + Expo, offline-first architecture, push notifications
  • Data Pipeline Engineering — ETL vs ELT, batch vs streaming, schema evolution
  • Labs: project setup & navigation, mobile inventory scanner, order tracking & supplier screens, offline-first architecture & mobile testing, data ingestion & transformation layers, data loading & orchestration (Airflow)

Day 3 — Microsoft Fabric Ontology, Testing & CI/CD

  • Microsoft Fabric Ontology for SCM — Entity Types, Properties, Relationships, Data Binding, the Ontology Graph, NL2Ontology natural language querying
  • AI-Powered Testing — test case generation, coverage analysis, edge case identification
  • Labs: Fabric workspace & ontology setup, defining the SCM ontology, data binding to OneLake, ontology graph exploration, NL2Ontology querying, E2E test generation (Playwright), performance & security testing (k6, OWASP ZAP), test execution & AI failure analysis

Day 4 — CI/CD, Monitoring & Production Readiness

  • CI/CD Pipeline with AI Assistance — GitHub Actions, Infrastructure as Code, deployment strategies, security gates
  • Monitoring, Observability & Production Readiness — logs, metrics, traces; AI-assisted incident response
  • Labs: GitHub Actions pipeline generation, deployment strategies (blue-green, canary), infrastructure as code (Kubernetes, Docker Compose), Prometheus & Grafana, log aggregation (Loki), distributed tracing (Jaeger/OpenTelemetry), AI-assisted incident response & documentation generation

Note: Labs are illustrative and built around a sample SCM domain project. Actual exercises may vary by trainer approach and participant profile.