Big Saas - Project | multi-ecom.md

🚀 Enterprise-Grade SaaS Platform: AI-Powered Business Intelligence for Multi-Channel E-Commerce

📊 Project Overview

Project Codename: OmniAnalytics Pro
Industry: E-Commerce Intelligence & Automation
Target Market: Mid-to-Large E-commerce Brands ($1M+ GMV) selling across multiple platforms (Shopify, Amazon, Walmart, Etsy, etc.)
Project Complexity: Enterprise-Level (6+ Years Experience Demonstrated)

🎯 The Business Problem

Modern e-commerce brands operate across 5-10 sales channels simultaneously. Each channel provides fragmented data, making it impossible to:

• Understand true customer lifetime value across channels
• Optimize inventory across multiple warehouses
• Calculate accurate unit economics when factoring in returns, fees, and channel-specific costs
• Make data-driven decisions about channel allocation and marketing spend

Current solutions (like SellerApp, Helium10, or custom Looker setups) are either too generic, too expensive ($ 50k+/year), or require extensive technical teams to maintain.

💡 Our Solution

A unified data warehouse + AI analytics layer that:

1. Ingests data from 15+ sources (APIs, CSVs, databases)
2. Normalizes it into a single source of truth
3. Applies ML models for predictions and recommendations
4. Provides actionable insights through customizable dashboards and automated alerts

---

🏗️ Architecture Requirements

Technical Stack (Modern & Demanding)

Backend: Laravel 11 (with PHP 8.3+ features: enums, readonly properties, fibers)
Database: PostgreSQL 16 + TimescaleDB (for time-series) + Redis (for caching/queues)
Real-time: Laravel Reverb (WebSockets) + Inertia.js + Vue 3 Composition API
Search: Elasticsearch 8.x (for product/customer search)
Queue: Laravel Horizon + Redis (for job monitoring)
AI/ML: Python microservices (FastAPI) + scikit-learn/TensorFlow
Infrastructure: Docker + Kubernetes (local dev) + AWS ECS (production)
Monitoring: Sentry + Prometheus + Grafana
Testing: PestPHP + Dusk (for browser tests) + Parallel testing

Multi-Tenant Architecture

Option 1: Database per Tenant (for enterprise clients)
Option 2: Schema per Tenant (for scaling)
Option 3: Row-level isolation with sophisticated sharding
Must support: 100+ tenants, 10M+ records per tenant, GDPR compliance

---

🔥 Core Features (Stretch Your Skills)

Module 1: Data Ingestion Engine

// This isn't just CSV import - it's a robust ETL pipeline
interface DataConnector {
    public function connect(): Connection;
    public function extract(Period $period): DataStream;
    public function transform(RawData $data): NormalizedData;
    public function load(NormalizedData $data): void;
    public function validate(): ValidationResult;
}

// Implement for:
- Shopify GraphQL API (with webhook handling)
- Amazon SP-API (OAuth 2.0 with refresh tokens)
- Walmart API (with rate limiting)
- Google Analytics 4 (via BigQuery)
- Facebook/Instagram Ads API
- QuickBooks Online (for accounting data)
- Custom FTP/SFTP sources
- Database direct connections (MySQL, Redshift)

Technical Challenges:

• Handle API rate limits with exponential backoff
• Implement idempotent imports (avoid duplicates)
• Process 100k+ records per import efficiently
• Real-time sync via webhooks
• Data validation with custom business rules per client

Module 2: Unified Data Model

Create a normalized schema that can represent data from any source:

-- Example: A "sale" can come from Shopify, Amazon, or POS system
CREATE TABLE unified_sales
(
    id            UUID PRIMARY KEY,
    tenant_id     UUID         NOT NULL,
    source_system VARCHAR(50)  NOT NULL, -- 'shopify', 'amazon', etc.
    source_id     VARCHAR(255) NOT NULL, -- Original ID from source
    sale_date     TIMESTAMPTZ  NOT NULL,
    customer_id   UUID REFERENCES unified_customers (id),
    gross_amount  DECIMAL(12, 2),
    net_amount    DECIMAL(12, 2),        -- After fees, returns
    currency_code CHAR(3),
    channel_id    UUID REFERENCES sales_channels (id),
    -- JSONB for source-specific data
    metadata      JSONB,
    -- Partition by tenant + month for performance
    UNIQUE (tenant_id, source_system, source_id)
) PARTITION BY HASH(tenant_id);

-- Create monthly partitions
CREATE TABLE unified_sales_tenant1_2024_01
    PARTITION OF unified_sales
    FOR VALUES WITH
(
    MODULUS
    4,
    REMAINDER
    0
);

Module 3: Real-time Analytics Engine

Build a OLAP cube for multi-dimensional analysis:

class AnalyticsCube {
    private array $dimensions = ['time', 'product', 'channel', 'customer_segment'];
    private array $measures = ['revenue', 'units', 'profit', 'aov'];
    
    public function query(CubeQuery $query): CubeResult {
        // Generate optimized SQL with:
        // - Materialized Views for common aggregations
        // - Window functions for running totals
        // - CTEs for complex calculations
        // - Query caching with Redis
    }
    
    public function precalculate(): void {
        // Background job to pre-calculate common queries
        // Use Laravel Horizon with priority queues
    }
}

Key Reports to Implement:

1. Customer Cohort Analysis: Retention by acquisition channel
2. Inventory Intelligence: Predict stockouts across channels
3. Channel Profitability: True profit after all costs
4. LTV vs CAC: By product category and channel
5. Return Rate Analysis: By product, channel, customer segment

Module 4: AI/ML Prediction Engine

# Separate Python service (FastAPI)
class DemandForecaster:
    def __init__(self):
        self.models = {
            'arima': ARIMAModel(),
            'prophet': ProphetModel(),
            'lstm': LSTMModel()
        }
    
    async def forecast(self, product_id: str, horizon_days: int) -> Forecast:
        # Ensemble multiple models
        # Consider: seasonality, promotions, competitor pricing
        # Return confidence intervals
        pass

class RecommendationEngine:
    def recommend_products(self, customer_id: str) -> List[Recommendation]:
        # Collaborative filtering + content-based
        # Real-time scoring
        pass

ML Models to Implement:

1. Demand Forecasting (6-month horizon)
2. Dynamic Pricing recommendations
3. Customer Churn Prediction
4. Return Likelihood scoring
5. Cross-sell/Up-sell recommendations

Module 5: Alerting & Automation System

class AlertEngine {
    public function evaluateRules(): void {
        // Evaluate business rules in real-time
        // Examples:
        // - Stock < safety_stock for 3 best-selling products
        // - CAC increased 20% in last 7 days
        // - Conversion rate dropped below threshold
        // - Competitor price 10% lower on key products
    }
    
    public function triggerAutomation(Action $action): void {
        // Connect to external systems:
        // - Adjust Facebook ad spend
        // - Update Shopify inventory levels
        // - Send Slack notifications to teams
        // - Create tasks in Asana/Jira
    }
}

Module 6: Advanced Dashboard & Visualization

• Real-time dashboards with WebSocket updates
• Custom report builder (drag-and-drop)
• Executive summaries with automated insights
• Mobile-responsive with PWA capabilities
• White-labeling for agencies/resellers

---

🗄️ Database Schema Highlights

Core Tables (Simplified View)

-- 1. Multi-tenancy with advanced features
CREATE TABLE tenants
(
    id                  UUID PRIMARY KEY,
    name                VARCHAR(255),
    plan_tier           VARCHAR(50),
    settings            JSONB, -- Custom configurations
    data_retention_days INTEGER DEFAULT 730,
    created_at          TIMESTAMPTZ,
    updated_at          TIMESTAMPTZ
);

-- 2. Time-series data optimized with TimescaleDB
CREATE TABLE metric_points
(
    time        TIMESTAMPTZ      NOT NULL,
    tenant_id   UUID             NOT NULL,
    metric_name VARCHAR(100)     NOT NULL,
    value       DOUBLE PRECISION NOT NULL,
    dimensions  JSONB, -- tags for filtering
    PRIMARY KEY (time, tenant_id, metric_name)
);

SELECT create_hypertable('metric_points', 'time');

-- 3. Graph-like relationships for customer journey
CREATE TABLE customer_journey_events
(
    customer_id       UUID,
    event_type        VARCHAR(50),
    event_time        TIMESTAMPTZ,
    channel           VARCHAR(50),
    campaign          VARCHAR(100),
    previous_event_id UUID, -- For sequence analysis
    properties        JSONB
);

-- GIN indexes for JSONB queries
CREATE INDEX idx_customer_journey_properties
    ON customer_journey_events USING GIN (properties);

Performance Optimizations Required

1. Partitioning: By tenant and time for large tables
2. Indexing Strategy:
   • B-tree for equality searches
   • BRIN for time-series
   • GIN for JSONB
   • Partial indexes for common filters
3. Materialized Views: For expensive aggregations
4. Query Optimization: Use EXPLAIN ANALYZE extensively
5. Connection Pooling: PgBouncer in production

---

⚡ Technical Challenges That Demonstrate 6+ Years Experience

Challenge 1: Real-time Data Sync at Scale

Problem: 100+ tenants, each with 10+ data sources, needing near-real-time updates.

Solution Architecture:

class DataSyncOrchestrator {
    use Queueable, InteractsWithQueue;
    
    public function handle(): void {
        // 1. Polling strategy per source type
        $strategies = [
            'shopify' => new WebhookStrategy(),
            'amazon' => new PollingStrategy(interval: '5m'),
            'ga4' => new BigQueryStreamingStrategy()
        ];
        
        // 2. Handle failures gracefully
        retry(3, 1000, function() {
            $this->syncTenantData($tenant);
        }, function($exception) {
            $this->notifyEngineering($exception);
            $this->logToSentry($exception);
        });
        
        // 3. Monitor sync health
        $this->emitMetrics($metrics);
    }
}

Challenge 2: Multi-tenant Query Optimization

Problem: Running complex queries across partitioned data without impacting other tenants.

Solution:

-- Use PostgreSQL query plan hints
EXPLAIN
(ANALYZE, BUFFERS)
WITH tenant_data AS (
    SELECT /*+ Materialize */ *
    FROM unified_sales
    WHERE tenant_id = ?
      AND sale_date BETWEEN ? AND ?
),
product_agg AS (
    SELECT 
        product_id,
        SUM(net_amount) as revenue,
        PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY net_amount) as median_sale
    FROM tenant_data
    GROUP BY product_id
    HAVING COUNT(*) > 10
)
SELECT *
FROM product_agg
ORDER BY revenue DESC LIMIT 100;

Challenge 3: Machine Learning Integration

Problem: Integrating Python ML models into PHP application seamlessly.

Solution:

# FastAPI service
@app.post("/forecast")
async def forecast(request: ForecastRequest):
    # Load model (cached)
    model = await load_model(request.model_type)
    
    # Get features from feature store
    features = await feature_store.get_features(
        request.product_ids, 
        request.horizon
    )
    
    # Generate predictions
    predictions = model.predict(features)
    
    # Return with confidence intervals
    return {
        "predictions": predictions.tolist(),
        "confidence": model.get_confidence_intervals(),
        "model_version": model.version
    }

// Laravel service calling ML API
class MLGateway {
    public function getForecast(array $productIds, int $days): array {
        return Http::timeout(30)
            ->retry(3, 100)
            ->withHeader('X-API-Key', config('ml.api_key'))
            ->post('https://ml-service/forecast', [
                'product_ids' => $productIds,
                'horizon' => $days,
                'model_type' => 'ensemble'
            ])
            ->throw()
            ->json();
    }
}

---

🧪 Testing Strategy (Enterprise Grade)

Test Pyramid

┌─────────────────┐
│  50 E2E Tests   │  (Browser tests with Dusk)
├─────────────────┤
│ 500 API Tests   │  (Feature tests with authentication)
├─────────────────┤
│ 2000 Unit Tests │  (Models, Services, Helpers)
├─────────────────┤
│   Load Tests    │  (k6 for 1000 concurrent users)
└─────────────────┘

Specific Tests Required

1. Multi-tenant data isolation tests
2. API rate limiting tests
3. Database migration rollback tests
4. Queue worker failure recovery tests
5. Cache invalidation tests
6. Internationalization tests
7. Accessibility tests
8. Security penetration tests (OWASP Top 10)

---

📈 Deployment & DevOps

Infrastructure as Code

# docker-compose.prod.yml
version: '3.8'
services:
  app:
    build: .
    environment:
      - APP_ENV=production
      - DB_HOST=postgres
      - REDIS_HOST=redis
    deploy:
      replicas: 4
      update_config:
        parallelism: 2
        delay: 10s
  
  postgres:
    image: timescale/timescaledb:latest-pg16
    volumes:
      - pgdata:/var/lib/postgresql/data
    command: >
      postgres 
      -c shared_preload_libraries=timescaledb
      -c max_connections=200
      
  ml-service:
    build: ./ml-service
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]

CI/CD Pipeline

# .github/workflows/deploy.yml
name: Deploy to Production

on:
  push:
    branches: [main]

jobs:
  test:
    runs-on: ubuntu-latest
    services:
      postgres:
        image: postgres:16
      redis:
        image: redis:7
    steps:
      - run: php artisan test --parallel
      - run: npm run test:e2e
      - run: k6 run load-test.js
  
  security-scan:
    runs-on: ubuntu-latest
    steps:
      - run: trivy image --exit-code 1 ${{ secrets.ECR_REPO }}
      - run: snyk test --sarif --json
      
  deploy:
    needs: [test, security-scan]
    runs-on: ubuntu-latest
    steps:
      - run: |
          aws ecs update-service \
            --cluster production \
            --service app \
            --force-new-deployment

---

🎯 Success Metrics & Deliverables

Phase 1: Foundation (Months 1-2)

• Multi-tenant architecture with proper isolation
• Data ingestion from 3 core sources (Shopify, CSV, Manual)
• Basic dashboard with key metrics
• Automated deployment pipeline
• Demonstrated Skill: Enterprise Laravel architecture

Phase 2: Scale (Months 3-4)

• 5+ data source integrations
• Real-time WebSocket updates
• Advanced reporting engine
• ML integration for basic forecasting
• Demonstrated Skill: Distributed systems design

Phase 3: Intelligence (Months 5-6)

• Full ML pipeline (training/deployment)
• Automated alerting system
• White-labeling capabilities
• API for third-party integrations
• Demonstrated Skill: AI/ML system integration

Phase 4: Enterprise (Months 7-8)

• Advanced security features (SSO, Audit logs)
• Custom workflow engine
• Performance optimization for 10M+ records
• Comprehensive documentation
• Demonstrated Skill: System optimization at scale

---

💼 Business Value & Portfolio Impact

Why This Proves 6+ Years Experience

1. Architecture: Microservices, event-driven, multi-tenant
2. Scale: Handles millions of records, real-time processing
3. Complexity: Integrates multiple external APIs, ML models
4. DevOps: Full CI/CD, containerization, monitoring
5. Testing: Comprehensive test suite at all levels
6. Security: Enterprise-grade authentication, data isolation
7. Performance: Optimized queries, caching strategy
8. UX: Professional dashboard with real-time updates

Portfolio Ready

• Live Demo: Deploy to AWS with sample data
• Code Repository: Well-documented, clean architecture
• Case Study: Document performance metrics (query times, etc.)
• Architecture Diagrams: System design documentation
• Blog Posts: Write about technical challenges solved

---

🚦 Getting Started (First 2 Weeks)

Week 1: Foundation Setup

# 1. Create project with modern stack
composer create-project laravel/laravel omni-analytics
cd omni-analytics

# 2. Set up Docker for local development
docker-compose up -d postgres redis elasticsearch

# 3. Initialize multi-tenancy
php artisan make:model Tenant -m
php artisan make:trait ScopesTenancy
php artisan make:middleware EnsureTenantSelected

# 4. Set up testing infrastructure
php artisan pest:install
npm install --save-dev @inertiajs/vue3 vue@next

Week 2: First Data Source

// Implement Shopify GraphQL client
class ShopifyClient {
    private Client $guzzle;
    
    public function fetchOrders(string $since = null): Collection {
        $query = <<<GRAPHQL
        query($since: String) {
          orders(first: 250, query: $since) {
            edges {
              node {
                id
                createdAt
                totalPriceSet {
                  shopMoney {
                    amount
                  }
                }
              }
            }
          }
        }
        GRAPHQL;
        
        return $this->executeQuery($query, ['since' => $since]);
    }
}

---

📚 Learning Resources & References

Must-Study Topics

1. Database: "Designing Data-Intensive Applications" by Martin Kleppmann
2. Laravel: Laravel Beyond CRUD by Freek Van der Herten
3. ML: "Hands-On Machine Learning with Scikit-Learn and TensorFlow"
4. Architecture: Microsoft's Cloud Design Patterns
5. Performance: "High Performance MySQL" by Baron Schwartz

Reference Implementations

• Metabase (Open-source BI tool)
• Saleor (GraphQL e-commerce)
• Invoice Ninja (Laravel multi-tenant)

---

🎖️ Final Certification

Once completed, you'll have demonstrated skills equivalent to:

• Senior Laravel Developer (architecture, scaling, performance)
• Data Engineer (ETL pipelines, data warehousing)
• MLOps Engineer (model deployment, monitoring)
• DevOps Engineer (CI/CD, containerization, monitoring)
• Product Engineer (UX, feature planning, user feedback)

This project, when completed with quality, will unquestionably position you as a 6+ years experienced developer ready for senior/lead roles at top tech companies or high-paying freelance contracts.

---

Ready to begin? Start with the multi-tenant foundation and build upward. Remember: Quality over speed. Each commit should be production-ready. Document your journey on LinkedIn/GitHub to build your reputation simultaneously.

First task: Set up the Docker environment and create the Tenant model with proper migrations.