Advanced Backend System: Complete Implementation Guide

Project Selection: Real-time Analytics Platform

Why This Project: Demonstrates the most advanced concepts while being practical. Combines streaming data, microservices, search, and real-time processing.

Business Use Case: A platform that ingests user events (clicks, purchases, page views), processes them in real-time, and provides analytics dashboards and alerts.

---

Architecture Overview

System Components

User Events → API Gateway → Quarkus Ingestion Service → RabbitMQ → Processing Services → Elasticsearch → Dashboard APIs
                    ↓
                Azure Blob (Raw Data Archive) + Azure Monitor (Observability)

Key Services

1. Event Ingestion Service (Quarkus) - Receives and validates events
2. Event Processing Service (Quarkus) - Aggregates and enriches data
3. Alert Service (Quarkus) - Monitors thresholds and sends notifications
4. Query Service (Quarkus) - Serves analytics data
5. Dashboard API (Quarkus) - Powers frontend dashboards

---

Technology Deep-Dive

Azure Services Used

• Azure Kubernetes Service (AKS): Container orchestration
• Azure Container Registry (ACR): Docker image storage
• Azure Blob Storage: Raw event archival and cold storage
• Azure Key Vault: Secrets management (centralized secret storage)
• Azure Event Hub: High-throughput event streaming (alternative/complement to RabbitMQ)
• Azure Functions: Serverless compute for specific tasks
• Azure Monitor/Application Insights: Observability
• Azure Load Balancer: Traffic distribution
• Azure Database for PostgreSQL: Configuration and user data

Enhanced Architecture with Your Links' Content

Hybrid Event Processing:

• Azure Event Hub for ultra-high throughput event ingestion (millions of events/second)
• RabbitMQ for complex routing, workflows, and reliable processing
• Azure Functions for serverless event processing and transformations

Complete Observability Stack:

• Elastic APM + Kibana for detailed application performance monitoring
• Azure Application Insights for Azure-native monitoring
• OpenTelemetry as the unified observability standard

Security & Configuration:

• Azure Key Vault integration for all secrets, certificates, and sensitive configuration
• Dynamic configuration retrieval without hardcoded secrets
• Native Compilation: GraalVM for fast startup and low memory
• Reactive Programming: Mutiny for non-blocking operations
• Health Checks: Built-in health endpoints
• Metrics: Micrometer integration with Prometheus
• OpenAPI: Automatic API documentation
• Fault Tolerance: Circuit breakers and retry policies
• Event-driven: MicroProfile Reactive Messaging

RabbitMQ Patterns

• Topic Exchanges: Route events by type and priority
• Dead Letter Queues: Handle failed message processing
• Message TTL: Expire old messages
• Priority Queues: Process critical events first
• Publisher Confirms: Ensure message delivery
• Consumer Acknowledgments: Reliable processing

Elasticsearch Capabilities

• Index Templates: Consistent mapping for time-series data
• Index Lifecycle Management: Automatic rollover and deletion
• Aggregations: Real-time analytics calculations
• Percolator Queries: Real-time alerting
• Alias Management: Zero-downtime index updates
• Cross-cluster Search: Scale across multiple clusters

---

Step-by-Step Implementation Plan

Phase 1: Infrastructure Setup (Week 1)

Step 1.1: Azure Environment Setup

# Create resource group
az group create --name analytics-platform-rg --location eastus

# Create AKS cluster
az aks create \
  --resource-group analytics-platform-rg \
  --name analytics-platform-aks \
  --node-count 3 \
  --node-vm-size Standard_D2s_v3 \
  --enable-addons monitoring \
  --generate-ssh-keys

# Create Azure Container Registry
az acr create \
  --resource-group analytics-platform-rg \
  --name analyticsplatformacr \
  --sku Standard \
  --admin-enabled true

Step 1.2: Terraform Infrastructure as Code

Create infrastructure modules:

• modules/aks/ - Kubernetes cluster configuration
• modules/storage/ - Blob storage and PostgreSQL
• modules/networking/ - VNet, subnets, security groups
• modules/monitoring/ - Application Insights, Log Analytics

Step 1.3: Kubernetes Manifests

Create Helm charts for:

• RabbitMQ cluster with persistence
• Elasticsearch cluster with proper node roles
• PostgreSQL for application data
• Ingress controller with SSL termination

Phase 2: Core Services Development (Week 2-3)

Step 2.1: Event Ingestion Service

Responsibilities: Receive, validate, and route events

Key Features:

• Rate limiting per client
• Event schema validation
• Async publishing to RabbitMQ
• Batch processing for high throughput

Implementation Highlights:

@ApplicationScoped
public class EventIngestionService {
    
    @Channel("events-out")
    Emitter<EventMessage> eventEmitter;
    
    @Timed(name = "event_ingestion_duration")
    @Counted(name = "events_received_total")
    public Uni<Void> ingestEvent(EventPayload payload) {
        return validateEvent(payload)
            .chain(this::enrichEvent)
            .chain(this::publishEvent)
            .onFailure().invoke(this::handleFailure);
    }
}

Step 2.2: Event Processing Service

Responsibilities: Aggregate events, calculate metrics, detect anomalies

Key Patterns:

• Event Sourcing: Store all events immutably
• CQRS: Separate read/write models
• Windowing: Time-based aggregations

Processing Pipeline:

1. Consume from RabbitMQ
2. Apply business rules
3. Update aggregations
4. Store in Elasticsearch
5. Trigger alerts if needed

Step 2.3: Alert Service

Responsibilities: Monitor thresholds and send notifications

Alert Types:

• Threshold alerts (e.g., error rate > 5%)
• Anomaly detection (statistical outliers)
• Pattern matching (specific event sequences)

Phase 3: Advanced Patterns Implementation (Week 4)

Step 3.1: Event Sourcing Pattern

@Entity
public class EventStore {
    private String aggregateId;
    private String eventType;
    private String eventData;
    private LocalDateTime timestamp;
    private Long version;
}

@ApplicationScoped
public class EventSourcingService {
    public Uni<Void> appendEvent(String aggregateId, DomainEvent event) {
        return persistEvent(aggregateId, event)
            .chain(() -> publishEvent(event))
            .chain(() -> updateProjections(event));
    }
}

Step 3.2: CQRS Implementation

• Command Side: Handle writes, validate business rules
• Query Side: Optimized read models in Elasticsearch
• Synchronization: Event-driven projection updates

Step 3.3: Saga Pattern for Complex Workflows

@ApplicationScoped
public class DataProcessingSaga {
    
    @Incoming("data-received")
    public Uni<Void> onDataReceived(DataReceivedEvent event) {
        return validateData(event)
            .chain(() -> transformData(event))
            .chain(() -> indexData(event))
            .onFailure().invoke(() -> compensateTransaction(event));
    }
}

Phase 4: Observability & Resilience (Week 5)

Step 4.1: Comprehensive Monitoring

Metrics Collection:

• Custom business metrics (events/second, processing latency)
• JVM metrics (memory, GC, threads)
• Infrastructure metrics (CPU, memory, network)

Distributed Tracing:

• Jaeger integration
• Trace correlation across services
• Performance bottleneck identification

Logging Strategy:

• Structured logging with JSON format
• Correlation IDs for request tracking
• Centralized logging with ELK stack

Step 4.2: Fault Tolerance Patterns

Circuit Breaker:

@CircuitBreaker(
    requestVolumeThreshold = 20,
    failureRatio = 0.5,
    delay = 5000
)
public Uni<ProcessingResult> processEvent(Event event) {
    return externalService.process(event);
}

Bulkhead Pattern:

• Separate thread pools for different operations
• Isolated resources for critical vs non-critical tasks

Retry with Backoff:

@Retry(
    maxRetries = 3,
    delay = 1000,
    jitter = 500
)
public Uni<Void> sendNotification(Alert alert) {
    return notificationService.send(alert);
}

Phase 5: Performance Optimization (Week 6)

Step 5.1: Elasticsearch Optimization

Index Design:

• Time-based indices (daily/weekly)
• Proper mapping for aggregations
• Index lifecycle management

Query Optimization:

• Use filters over queries when possible
• Implement caching for frequent queries
• Optimize aggregation performance

Step 5.2: RabbitMQ Tuning

Performance Settings:

• Adjust prefetch count for consumers
• Use lazy queues for large backlogs
• Implement message deduplication

High Availability:

• Quorum queues for critical data
• Cross-AZ deployment
• Automatic failover configuration

Step 5.3: Quarkus Optimization

Native Compilation:

• GraalVM native image for production
• Reflection configuration for libraries
• Build optimization flags

Resource Management:

• Connection pooling configuration
• Memory allocation tuning
• Garbage collection optimization

Phase 6: Security Implementation (Week 7)

Step 6.1: Authentication & Authorization

• OAuth2/JWT token validation
• Role-based access control (RBAC)
• API key management for external clients

Step 6.2: Data Protection

• Encryption at rest (Azure Storage Service Encryption)
• Encryption in transit (TLS 1.3)
• PII data masking/anonymization

Step 6.3: Network Security

• Azure Network Security Groups
• Private endpoints for internal communication
• Web Application Firewall (WAF) rules

Phase 7: Testing Strategy (Week 8)

Step 7.1: Test Pyramid Implementation

Unit Tests:

• Business logic validation
• Mock external dependencies
• Fast feedback loop

Integration Tests:

• Testcontainers for external services
• End-to-end workflow testing
• Performance regression tests

Contract Tests:

• Pact consumer/provider testing
• API schema validation
• Backward compatibility checks

Step 7.2: Load Testing

Scenarios:

• Normal load (baseline performance)
• Peak load (2x normal traffic)
• Stress testing (breaking point)
• Endurance testing (sustained load)

Tools: JMeter, Gatling, or custom Quarkus test harness

Phase 8: CI/CD Pipeline (Week 9)

Step 8.1: Build Pipeline

# Azure DevOps Pipeline
stages:
- stage: Build
  jobs:
  - job: BuildServices
    steps:
    - task: Maven@3
      inputs:
        goals: 'clean compile test'
    - task: Docker@2
      inputs:
        command: 'buildAndPush'
        repository: '$(imageRepository)'
        tags: '$(Build.BuildId)'

- stage: Deploy
  jobs:
  - deployment: DeployToStaging
    environment: 'staging'
    strategy:
      runOnce:
        deploy:
          steps:
          - task: HelmDeploy@0
            inputs:
              command: 'upgrade'
              chartPath: './helm-charts'

Step 8.2: Deployment Strategy

• Blue-Green Deployment: Zero-downtime updates
• Canary Releases: Gradual rollout with monitoring
• Feature Flags: Toggle features without deployment

Phase 9: Advanced Features (Week 10-11)

Step 9.1: Machine Learning Integration

• Anomaly detection using Azure ML
• Predictive analytics for capacity planning
• Real-time recommendation engine

Step 9.2: Multi-tenancy Support

• Tenant isolation strategies
• Resource quotas and billing
• Custom configurations per tenant

Step 9.3: Data Lake Integration

• Stream events to Azure Data Lake
• Historical data analysis
• Batch processing with Apache Spark

Phase 10: Production Readiness (Week 12)

Step 10.1: Operational Procedures

• Runbook creation
• Incident response procedures
• Capacity planning guidelines
• Backup and recovery strategies

Step 10.2: Performance Benchmarks

• Establish SLA targets
• Create performance dashboards
• Set up alerting thresholds
• Document scaling procedures

---

Architecture Patterns Demonstrated

1. Event Sourcing

Benefits: Complete audit trail, time-travel debugging, replay capability Implementation: Store all state changes as events, rebuild current state from events

2. CQRS (Command Query Responsibility Segregation)

Benefits: Optimized read/write models, independent scaling Implementation: Separate command handlers from query handlers

3. Saga Pattern

Benefits: Manage distributed transactions, failure recovery Implementation: Choreography-based sagas with compensating actions

4. Circuit Breaker

Benefits: Prevent cascade failures, graceful degradation Implementation: Monitor failure rates, open circuit when threshold exceeded

5. Bulkhead

Benefits: Fault isolation, resource protection Implementation: Separate thread pools and connection pools

6. Outbox Pattern

Benefits: Reliable event publishing, transactional guarantees Implementation: Store events in database, separate publisher process

---

Key Learning Outcomes

Technical Skills Demonstrated

• Microservices Architecture: Service decomposition, inter-service communication
• Event-Driven Design: Async processing, eventual consistency
• Cloud-Native Development: Container deployment, cloud services integration
• Performance Engineering: Optimization techniques, scalability patterns
• Observability: Monitoring, logging, tracing strategies
• Security: Authentication, authorization, data protection
• Testing: Comprehensive test strategy, automation
• DevOps: CI/CD, infrastructure as code, deployment strategies

Business Value

• Real-time Processing: Sub-second event processing
• Scalability: Handle 10K+ events/second
• Reliability: 99.9% uptime with fault tolerance
• Cost Efficiency: Auto-scaling based on demand
• Insights: Real-time analytics and alerting

---

Advanced Topics for Further Development

1. Multi-Region Deployment

• Cross-region replication
• Global load balancing
• Data consistency across regions

2. Stream Processing with Apache Kafka

• Replace RabbitMQ with Kafka for higher throughput
• Kafka Streams for complex event processing
• Schema registry for event evolution

3. GraphQL API Layer

• Unified data access layer
• Real-time subscriptions
• Efficient data fetching

4. Event Mesh Architecture

• Decentralized event routing
• Event catalog and governance
• Cross-team event sharing

This implementation showcases enterprise-grade backend development skills and demonstrates mastery of modern distributed systems patterns. The combination of technologies you suggested works excellently together and provides a solid foundation for building scalable, resilient systems.

analytics-platform/ │ ├── README.md ├── .gitignore ├── docker-compose.yml # Local development environment ├── docker-compose.prod.yml # Production-like local setup │ ├── infrastructure/ # Infrastructure as Code │ ├── terraform/ │ │ ├── main.tf │ │ ├── variables.tf │ │ ├── outputs.tf │ │ └── modules/ │ │ ├── aks/ │ │ │ ├── main.tf │ │ │ ├── variables.tf │ │ │ └── outputs.tf │ │ ├── acr/ │ │ ├── keyvault/ │ │ ├── eventhub/ │ │ ├── storage/ │ │ └── monitoring/ │ │ │ ├── kubernetes/ # K8s manifests │ │ ├── namespaces/ │ │ ├── configmaps/ │ │ ├── secrets/ │ │ ├── deployments/ │ │ ├── services/ │ │ ├── ingress/ │ │ └── monitoring/ │ │ │ └── helm/ # Helm charts │ ├── analytics-platform/ │ │ ├── Chart.yaml │ │ ├── values.yaml │ │ ├── values-dev.yaml │ │ ├── values-prod.yaml │ │ └── templates/ │ ├── rabbitmq/ │ ├── elasticsearch/ │ └── monitoring/ │ ├── services/ # Microservices │ │ │ ├── event-ingestion/ # Main event ingestion service │ │ ├── src/ │ │ │ ├── main/ │ │ │ │ ├── java/ │ │ │ │ │ └── com/analytics/ingestion/ │ │ │ │ │ ├── EventIngestionApplication.java │ │ │ │ │ ├── config/ │ │ │ │ │ │ ├── AzureConfig.java │ │ │ │ │ │ ├── RabbitMQConfig.java │ │ │ │ │ │ └── SecurityConfig.java │ │ │ │ │ ├── controller/ │ │ │ │ │ │ ├── EventController.java │ │ │ │ │ │ └── HealthController.java │ │ │ │ │ ├── service/ │ │ │ │ │ │ ├── EventIngestionService.java │ │ │ │ │ │ ├── ValidationService.java │ │ │ │ │ │ └── PublishingService.java │ │ │ │ │ ├── model/ │ │ │ │ │ │ ├── EventPayload.java │ │ │ │ │ │ ├── EventMetadata.java │ │ │ │ │ │ └── ValidationResult.java │ │ │ │ │ ├── messaging/ │ │ │ │ │ │ ├── EventProducer.java │ │ │ │ │ │ └── EventConsumer.java │ │ │ │ │ └── exception/ │ │ │ │ │ ├── ValidationException.java │ │ │ │ │ └── PublishingException.java │ │ │ │ └── resources/ │ │ │ │ ├── application.yml │ │ │ │ ├── application-dev.yml │ │ │ │ ├── application-prod.yml │ │ │ │ └── META-INF/ │ │ │ │ └── native-image/ │ │ │ └── test/ │ │ │ ├── java/ │ │ │ │ └── com/analytics/ingestion/ │ │ │ │ ├── EventIngestionServiceTest.java │ │ │ │ ├── integration/ │ │ │ │ │ ├── EventIngestionIntegrationTest.java │ │ │ │ │ └── TestContainersConfig.java │ │ │ │ └── contract/ │ │ │ │ └── EventApiContractTest.java │ │ │ └── resources/ │ │ │ ├── application-test.yml │ │ │ └── test-data/ │ │ ├── pom.xml │ │ ├── Dockerfile │ │ ├── Dockerfile.native │ │ └── .dockerignore │ │ │ ├── event-processing/ # Event processing and aggregation │ │ ├── src/ │ │ │ ├── main/ │ │ │ │ ├── java/ │ │ │ │ │ └── com/analytics/processing/ │ │ │ │ │ ├── EventProcessingApplication.java │ │ │ │ │ ├── processor/ │ │ │ │ │ │ ├── EventProcessor.java │ │ │ │ │ │ ├── AggregationProcessor.java │ │ │ │ │ │ └── EnrichmentProcessor.java │ │ │ │ │ ├── saga/ │ │ │ │ │ │ ├── DataProcessingSaga.java │ │ │ │ │ │ └── CompensationHandler.java │ │ │ │ │ ├── eventsourcing/ │ │ │ │ │ │ ├── EventStore.java │ │ │ │ │ │ ├── EventSourcingService.java │ │ │ │ │ │ └── ProjectionUpdater.java │ │ │ │ │ └── cqrs/ │ │ │ │ │ ├── command/ │ │ │ │ │ │ ├── CommandHandler.java │ │ │ │ │ │ └── ProcessEventCommand.java │ │ │ │ │ └── query/ │ │ │ │ │ ├── QueryHandler.java │ │ │ │ │ └── EventQueryService.java │ │ │ │ └── resources/ │ │ │ │ └── application.yml │ │ │ └── test/ │ │ ├── pom.xml │ │ └── Dockerfile │ │ │ ├── alert-service/ # Alerting and notifications │ │ ├── src/ │ │ │ ├── main/ │ │ │ │ ├── java/ │ │ │ │ │ └── com/analytics/alerts/ │ │ │ │ │ ├── AlertServiceApplication.java │ │ │ │ │ ├── detector/ │ │ │ │ │ │ ├── ThresholdDetector.java │ │ │ │ │ │ ├── AnomalyDetector.java │ │ │ │ │ │ └── PatternDetector.java │ │ │ │ │ ├── notification/ │ │ │ │ │ │ ├── NotificationService.java │ │ │ │ │ │ ├── EmailNotifier.java │ │ │ │ │ │ └── SlackNotifier.java │ │ │ │ │ └── resilience/ │ │ │ │ │ ├── CircuitBreakerConfig.java │ │ │ │ │ └── RetryConfig.java │ │ │ │ └── resources/ │ │ │ └── test/ │ │ ├── pom.xml │ │ └── Dockerfile │ │ │ ├── query-service/ # Analytics query API │ │ ├── src/ │ │ │ ├── main/ │ │ │ │ ├── java/ │ │ │ │ │ └── com/analytics/query/ │ │ │ │ │ ├── QueryServiceApplication.java │ │ │ │ │ ├── controller/ │ │ │ │ │ │ ├── AnalyticsController.java │ │ │ │ │ │ └── MetricsController.java │ │ │ │ │ ├── service/ │ │ │ │ │ │ ├── ElasticsearchService.java │ │ │ │ │ │ └── QueryOptimizer.java │ │ │ │ │ └── dto/ │ │ │ │ │ ├── QueryRequest.java │ │ │ │ │ └── AnalyticsResponse.java │ │ │ │ └── resources/ │ │ │ └── test/ │ │ ├── pom.xml │ │ └── Dockerfile │ │ │ └── dashboard-api/ # Dashboard backend API │ ├── src/ │ │ ├── main/ │ │ │ ├── java/ │ │ │ │ └── com/analytics/dashboard/ │ │ │ │ ├── DashboardApiApplication.java │ │ │ │ ├── controller/ │ │ │ │ ├── service/ │ │ │ │ └── websocket/ │ │ │ │ └── RealTimeDashboardWebSocket.java │ │ │ └── resources/ │ │ └── test/ │ ├── pom.xml │ └── Dockerfile │ ├── azure-functions/ # Serverless functions │ ├── event-transformer/ │ │ ├── src/ │ │ │ └── main/ │ │ │ └── java/ │ │ │ └── com/analytics/functions/ │ │ │ ├── EventTransformerFunction.java │ │ │ └── EventHubTriggerFunction.java │ │ ├── pom.xml │ │ └── host.json │ │ │ └── data-archiver/ │ ├── src/ │ └── pom.xml │ ├── shared/ # Shared libraries │ ├── common-models/ │ │ ├── src/ │ │ │ └── main/ │ │ │ └── java/ │ │ │ └── com/analytics/common/ │ │ │ ├── model/ │ │ │ │ ├── Event.java │ │ │ │ ├── User.java │ │ │ │ └── Tenant.java │ │ │ ├── dto/ │ │ │ │ ├── EventDto.java │ │ │ │ └── ResponseDto.java │ │ │ └── constants/ │ │ │ ├── EventTypes.java │ │ │ └── MessageQueues.java │ │ └── pom.xml │ │ │ ├── security/ │ │ ├── src/ │ │ │ └── main/ │ │ │ └── java/ │ │ │ └── com/analytics/security/ │ │ │ ├── jwt/ │ │ │ ├── oauth/ │ │ │ └── keyvault/ │ │ │ └── AzureKeyVaultClient.java │ │ └── pom.xml │ │ │ └── observability/ │ ├── src/ │ │ └── main/ │ │ └── java/ │ │ └── com/analytics/observability/ │ │ ├── tracing/ │ │ │ ├── TracingConfig.java │ │ │ └── CustomTracer.java │ │ ├── metrics/ │ │ │ ├── CustomMetrics.java │ │ │ └── BusinessMetrics.java │ │ └── logging/ │ │ ├── LoggingConfig.java │ │ └── StructuredLogger.java │ └── pom.xml │ ├── monitoring/ # Observability configuration │ ├── elastic-apm/ │ │ ├── apm-server.yml │ │ └── elasticsearch-template.json │ │ │ ├── prometheus/ │ │ ├── prometheus.yml │ │ └── alert-rules.yml │ │ │ ├── grafana/ │ │ ├── dashboards/ │ │ │ ├── application-metrics.json │ │ │ ├── business-metrics.json │ │ │ └── infrastructure-metrics.json │ │ └── datasources/ │ │ └── datasources.yml │ │ │ └── kibana/ │ ├── index-patterns/ │ ├── visualizations/ │ └── dashboards/ │ ├── config/ # Configuration files │ ├── elasticsearch/ │ │ ├── index-templates/ │ │ │ ├── events-template.json │ │ │ └── metrics-template.json │ │ ├── index-policies/ │ │ │ └── lifecycle-policy.json │ │ └── mappings/ │ │ ├── event-mapping.json │ │ └── metric-mapping.json │ │ │ ├── rabbitmq/ │ │ ├── definitions.json │ │ ├── exchanges.json │ │ └── queues.json │ │ │ └── azure/ │ ├── eventhub-config.json │ └── keyvault-config.json │ ├── docs/ # Documentation │ ├── architecture/ │ │ ├── system-design.md │ │ ├── data-flow.md │ │ └── patterns.md │ │ │ ├── deployment/ │ │ ├── local-setup.md │ │ ├── azure-deployment.md │ │ └── troubleshooting.md │ │ │ ├── api/ │ │ ├── event-ingestion-api.md │ │ ├── query-api.md │ │ └── postman-collections/ │ │ │ └── runbooks/ │ ├── incident-response.md │ ├── scaling-procedures.md │ └── backup-recovery.md │ ├── scripts/ # Automation scripts │ ├── build/ │ │ ├── build-all.sh │ │ ├── build-native.sh │ │ └── docker-build.sh │ │ │ ├── deployment/ │ │ ├── deploy-dev.sh │ │ ├── deploy-prod.sh │ │ └── rollback.sh │ │ │ ├── database/ │ │ ├── setup-elasticsearch.sh │ │ └── create-indices.sh │ │ │ └── local-dev/ │ ├── start-local.sh │ ├── stop-local.sh │ └── reset-data.sh │ ├── tests/ # Integration and E2E tests │ ├── integration/ │ │ ├── src/ │ │ │ └── test/ │ │ │ └── java/ │ │ │ └── com/analytics/integration/ │ │ │ ├── EventFlowIntegrationTest.java │ │ │ ├── AlertingIntegrationTest.java │ │ │ └── QueryPerformanceTest.java │ │ └── pom.xml │ │ │ ├── performance/ │ │ ├── jmeter/ │ │ │ ├── event-ingestion-load-test.jmx │ │ │ └── query-performance-test.jmx │ │ ├── gatling/ │ │ │ └── EventIngestionSimulation.scala │ │ └── reports/ │ │ │ └── contract/ │ ├── pacts/ │ └── consumer-tests/ │ ├── ci-cd/ # CI/CD pipeline definitions │ ├── azure-pipelines/ │ │ ├── build-pipeline.yml │ │ ├── deploy-pipeline.yml │ │ └── release-pipeline.yml │ │ │ ├── github-actions/ │ │ ├── .github/ │ │ │ └── workflows/ │ │ │ ├── ci.yml │ │ │ ├── cd.yml │ │ │ └── security-scan.yml │ │
│ └── jenkins/ │ └── Jenkinsfile │ ├── security/ # Security configurations │ ├── policies/ │ │ ├── azure-policies.json │ │ └── rbac-definitions.json │ │ │ ├── certificates/ │ │ └── .gitkeep │ │ │ └── scanning/ │ ├── sonarqube-config.xml │ └── dependency-check-config.xml │ ├── data/ # Sample and test data │ ├── sample-events/ │ │ ├── user-events.json │ │ ├── system-events.json │ │ └── error-events.json │ │ │ ├── schemas/ │ │ ├── event-schema.json │ │ └── metric-schema.json │ │ │ └── migrations/ │ └── elasticsearch-mappings/ │ ├── tools/ # Development tools │ ├── code-generation/ │ │ ├── openapi-generator-config.json │ │ └── generate-clients.sh │ │ │ ├── local-dev/ │ │ ├── docker-compose-full.yml │ │ ├── docker-compose-minimal.yml │ │ └── env-templates/ │ │ ├── .env.development │ │ └── .env.testing │ │ │ └── debugging/ │ ├── log-analysis.sh │ └── performance-profiling.sh │ ├── pom.xml # Parent POM for all services ├── .gitignore ├── .editorconfig ├── LICENSE └── CHANGELOG.md

Core Architecture Principles

🏗️ Microservices Pattern: Each service in /services/ is independently deployable and scalable 🔄 Event-Driven Design: Clear separation between event ingestion, processing, and querying ☁️ Cloud-Native: Azure-first with Kubernetes orchestration and serverless components 📊 Observability-First: Comprehensive monitoring, tracing, and alerting built-in

What This Structure Demonstrates

Advanced Backend Skills

Distributed Systems: Event sourcing, CQRS, saga patterns Scalability: Microservices, message queues, auto-scaling Resilience: Circuit breakers, retries, health checks Security: Azure Key Vault, OAuth2, secrets management

DevOps & Infrastructure

Infrastructure as Code: Terraform for Azure resources Container Orchestration: Kubernetes with Helm charts CI/CD: Azure DevOps and GitHub Actions pipelines Monitoring: Elastic APM, Prometheus, Grafana stack

Enterprise Practices

Testing Strategy: Unit, integration, contract, and performance tests Documentation: Architecture docs, runbooks, API documentation Security: Policies, scanning, certificate management Compliance: Audit trails, data governance, backup procedures

Technology Integration Points

Your Links Integration:

Azure Key Vault: Centralized in /shared/security/keyvault/ Azure Event Hub: Configuration in /config/azure/eventhub-config.json Elastic APM: Complete setup in /monitoring/elastic-apm/ Azure Functions: Serverless components in /azure-functions/

The structure shows this isn't just a learning project - it's a portfolio-worthy system that demonstrates the kind of architecture used in Fortune 500 companies for handling millions of events per day. Would you like me to dive deeper into any specific folder or show you how to set up a particular service from this structure?