Order Analytics Platform

A comprehensive Data Warehouse + Real-Time Analytics Platform combining transactional microservices with a cloud-native data pipeline.

🏗️ Architecture

┌───────────────────────────────────────────────────────────────────┐
│            OPERATIONAL LAYER (GKE/Cloud Run)                      │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌─────────────┐        │
│  │ Order API│  │ Payment  │  │Inventory │  │  Shipping   │        │
│  │ (WebFlux)│  │ Service  │  │ Service  │  │   Service   │        │
│  └────┬─────┘  └────┬─────┘  └────┬─────┘  └──────┬──────┘        │
│       │             │             │               │               │
│       └─────────────┴─────────────┴───────────────┘               │
│                             │                                     │
│                    ┌────────▼─────────┐                           │
│                    │  Kafka Cluster   │                           │
│                    │  (Event Stream)  │                           │
│                    └────────┬─────────┘                           │
└─────────────────────────────┼─────────────────────────────────────┘
                              │
┌─────────────────────────────▼─────────────────────────────────────┐
│                    INGESTION LAYER (GCP + K8s)                    │
│  ┌─────────────┐  ┌─────────────┐  ┌──────────────┐               │
│  │ Debezium    │  │  Pub/Sub    │  │  Dataflow    │               │
│  │   CDC       │  │  (Events)   │  │  (Streaming) │               │
│  └──────┬──────┘  └──────┬──────┘  └──────┬───────┘               │
│         │                │                │                       │
│         └────────────────┴────────────────┘                       │
│                          │                                        │
└──────────────────────────┼────────────────────────────────────────┘
                           │
┌──────────────────────────▼───────────────────────────────────────┐
│                  RAW DATA LAYER (GCS + BigQuery)                 │
│  ┌────────────────┐  ┌────────────────┐  ┌─────────────────┐     │
│  │  GCS Buckets   │  │   BigQuery     │  │   Bigtable      │     │
│  │ (Data Lake)    │  │  (Raw Tables)  │  │  (Time-Series)  │     │
│  │ - bronze/      │  │ - raw_orders   │  │ - event_stream  │     │
│  │ - silver/      │  │ - raw_payments │  │ - clickstream   │     │
│  │ - gold/        │  │ - raw_shipping │  └─────────────────┘     │
│  └───────┬────────┘  └───────┬────────┘                          │
└──────────┼───────────────────┼───────────────────────────────────┘
           │                   │
┌──────────▼───────────────────▼────────────────────────────────────┐
│              TRANSFORMATION LAYER (dbt + Dataform)                │
│  ┌────────────────────────────────────────────────────┐           │
│  │                dbt Models (SQL)                    │           │
│  │  ┌──────────┐  ┌──────────┐  ┌──────────────┐      │           │
│  │  │ Staging  │→ │ Marts    │→ │ Aggregates   │      │           │
│  │  │ (Clean)  │  │(Business)│  │ (Metrics)    │      │           │
│  │  └──────────┘  └──────────┘  └──────────────┘      │           │
│  └────────────────────────────────────────────────────┘           │
│  Data Quality: Great Expectations + Soda                          │
└──────────────────────────┬────────────────────────────────────────┘
                           │
┌──────────────────────────▼───────────────────────────────────────┐
│                 SERVING LAYER (BigQuery + GCS)                   │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐            │
│  │ Dimensional  │  │  Aggregate   │  │   ML Ready   │            │
│  │   Models     │  │   Tables     │  │   Features   │            │
│  │ - dim_*      │  │ - fact_*     │  │ - feature_*  │            │
│  │ - fact_*     │  │ - metrics_*  │  └──────────────┘            │
│  └──────────────┘  └──────────────┘                              │
└──────────────────────────┬───────────────────────────────────────┘
                           │
┌──────────────────────────▼───────────────────────────────────────┐
│              CONSUMPTION LAYER (BI + Apps)                       │
│  ┌─────────────┐  ┌────────────┐  ┌─────────────┐                │
│  │  React UI   │  │  Grafana   │  │   Looker    │                │
│  │ (Dashboard) │  │ (Metrics)  │  │  (Reports)  │                │
│  └─────────────┘  └────────────┘  └─────────────┘                │
└──────────────────────────────────────────────────────────────────┘

          ┌─────────────────────────────────────────────────┐
          │    ORCHESTRATION: Cloud Composer (Airflow)      │
          │    INFRASTRUCTURE: Terraform + Helm             │
          │    GOVERNANCE: Dataplex + Data Catalog          │
          └─────────────────────────────────────────────────┘

📦 Project Structure

order-analytics-platform/
├── terraform/              # Infrastructure as Code
│   ├── modules/           # Reusable Terraform modules
│   │   ├── bigquery/      # BigQuery datasets, tables, views
│   │   ├── gke-cluster/   # GKE cluster (optional - can use Cloud Run)
│   │   ├── cloud-run/     # Cloud Run services (recommended)
│   │   ├── vpc/           # VPC, subnets, firewall rules
│   │   ├── pubsub/        # Pub/Sub topics and subscriptions
│   │   ├── composer/      # Cloud Composer (Airflow)
│   │   ├── gcs-buckets/   # GCS buckets for data lake
│   │   ├── iam/           # Service accounts and IAM roles
│   │   └── cloud-sql/     # PostgreSQL (operational DB)
│   └── environments/      # Environment-specific configs
│       ├── dev/
│       ├── staging/
│       └── prod/
├── scripts/             # Deployment and utility scripts
│   ├── deploy-cloud-run.sh    # Deploy services to Cloud Run
│   ├── setup-database.sh       # Initialize databases
│   └── quick-start.sh          # Quick setup script
├── dbt/                  # Data transformations
│   ├── models/
│   │   ├── staging/      # Cleaned source tables
│   │   ├── marts/        # Business logic models
│   │   └── aggregates/   # Pre-aggregated metrics
│   ├── tests/            # Data quality tests
│   ├── macros/           # Reusable SQL functions
│   └── seeds/            # Static lookup data
├── airflow/              # Orchestration
│   ├── dags/            # DAG definitions
│   ├── plugins/         # Custom operators
│   └── config/          # Airflow configuration
├── services/            # Microservices
│   ├── order-service/   # Spring Boot WebFlux
│   ├── payment-service/ # Payment processing
│   ├── inventory-service/ # Inventory management
│   ├── shipping-service/ # Shipping logic
│   ├── saga-coordinator/ # Saga orchestration
│   ├── analytics-api/   # Analytics API
│   └── api-gateway/     # Spring Cloud Gateway
├── ui-vite/             # React Dashboard
│   ├── public/
│   └── src/
│       ├── components/  # Reusable UI components
│       ├── pages/       # Page-level components
│       ├── services/    # API clients
│       └── utils/       # Helper functions
├── docs/                # Documentation
└── dataflow/            # Dataflow pipelines

🚀 Quick Start

Prerequisites

GCP Account with billing enabled
Terraform >= 1.6.0
kubectl >= 1.28.0
gcloud CLI >= 450.0.0
Docker >= 24.0.0
Node.js >= 18.0.0 (for React UI)
dbt >= 1.7.0

1. Setup GCP Project

# Set environment variables
export PROJECT_ID="order-analytics-platform"
export REGION="asia-south1"
export ENV="dev"

# Authenticate with GCP
gcloud auth login
gcloud config set project $PROJECT_ID

# Enable required APIs
gcloud services enable \
  compute.googleapis.com \
  container.googleapis.com \
  bigquery.googleapis.com \
  composer.googleapis.com \
  pubsub.googleapis.com \
  storage-api.googleapis.com \
  dataflow.googleapis.com

2. Deploy Infrastructure with Terraform

cd terraform

# Initialize Terraform
terraform init

# Plan infrastructure changes
terraform plan -var-file=environments/$ENV/terraform.tfvars

# Deploy infrastructure
terraform apply -var-file=environments/$ENV/terraform.tfvars

This creates:

Cloud Run services for all microservices (or GKE cluster if preferred)
BigQuery datasets (raw, staging, prod)
GCS buckets (data lake with bronze/silver/gold layers)
Cloud Composer environment (Airflow)
Cloud SQL PostgreSQL instance
Pub/Sub topics for CDC
VPC network with private subnets
Service accounts with IAM roles

3. Deploy Microservices to Cloud Run

# Deploy all services
bash scripts/deploy-cloud-run.sh dev

# Or deploy individual services
gcloud run deploy order-service-dev \
  --image=gcr.io/$PROJECT_ID/order-service:latest \
  --region=$REGION \
  --allow-unauthenticated \
  --port=8081

Alternative: Deploy to GKE (if using Kubernetes)

# Get GKE credentials
gcloud container clusters get-credentials order-platform-$ENV --region=$REGION

# Apply Kubernetes manifests (if using GKE)
kubectl apply -f kubernetes/base/

# Verify deployment
kubectl get pods -n order-system

4. Setup dbt

cd dbt

# Install dbt dependencies
pip install dbt-bigquery

# Configure dbt profile
cat > ~/.dbt/profiles.yml <<EOF
order_analytics:
  target: $ENV
  outputs:
    dev:
      type: bigquery
      method: service-account
      project: $PROJECT_ID
      dataset: staging_dev
      keyfile: /path/to/service-account.json
      location: $REGION
      threads: 4
EOF

# Run dbt models
dbt run --target=$ENV

# Run dbt tests
dbt test --target=$ENV

5. Deploy Airflow DAGs

cd airflow

# Upload DAGs to Cloud Composer
gcloud composer environments storage dags import \
  --environment=order-data-pipeline-$ENV \
  --location=$REGION \
  --source=dags/

# Trigger DAG manually
gcloud composer environments run order-data-pipeline-$ENV \
  --location=$REGION \
  dags trigger -- daily_etl_pipeline

6. Start React UI

cd ui

# Install dependencies
npm install

# Configure environment
cat > .env.local <<EOF
REACT_APP_API_URL=http://localhost:8080
REACT_APP_BIGQUERY_PROJECT=$PROJECT_ID
REACT_APP_ENV=$ENV
EOF

# Start development server
npm start

# Build for production
npm run build

Access UI at: http://localhost:3000

📊 Data Flow

Transactional Flow (Real-Time)

Order API → PostgreSQL → Debezium → Kafka → BigQuery Sink → BigQuery (raw)
  ↓                                   ↓
Kafka Streams                    Pub/Sub → Dataflow → BigQuery

Analytical Flow (Batch)

BigQuery (raw) → dbt Staging → dbt Marts → BigQuery (prod)
                     ↓
              Data Quality Tests (Great Expectations)
                     ↓
              Airflow DAG Orchestration

Query Flow

React UI → API Gateway → BigQuery REST API → BigQuery (prod tables)
                ↓
         Cache Layer (Redis)

🎯 Key Features

1. Infrastructure as Code (Terraform)

✅ Modular design with reusable components
✅ Environment separation (dev/staging/prod)
✅ Remote state management in GCS
✅ Automated CI/CD with GitHub Actions

2. Data Warehouse (BigQuery)

✅ Medallion architecture (bronze/silver/gold)
✅ Partitioning by date for query optimization
✅ Clustering on high-cardinality columns
✅ Materialized views for fast aggregations
✅ Dimensional modeling (star schema)

3. Data Transformations (dbt)

✅ Incremental models for efficiency
✅ Staging → Marts → Aggregates pipeline
✅ Built-in data quality tests
✅ Macros for reusable SQL logic
✅ Documentation generation

4. Orchestration (Airflow)

✅ DAG-based workflow management
✅ SLA monitoring and alerting
✅ Retry logic with exponential backoff
✅ Integration with dbt, BigQuery, Dataflow
✅ Custom operators for business logic

5. Real-Time Streaming

✅ Kafka for event streaming
✅ Debezium CDC from PostgreSQL
✅ Kafka Streams for aggregations
✅ Sub-second latency for critical events

6. Microservices (Cloud Run/GKE)

✅ Spring Boot WebFlux (reactive)
✅ Circuit Breaker pattern (Resilience4j)
✅ Saga pattern for distributed transactions
✅ Auto-scaling (Cloud Run scales to zero)
✅ Serverless or Kubernetes deployment options

7. React Dashboard

✅ Real-time metrics visualization
✅ Interactive charts (Recharts, D3.js)
✅ Order management interface
✅ Data quality monitoring
✅ Pipeline observability

🔍 Monitoring & Observability

Metrics

Prometheus for metrics collection
Grafana for dashboards
Custom metrics for pipeline health

Logging

Cloud Logging for centralized logs
Log aggregation from Kubernetes pods
Airflow task logs

Tracing

Jaeger for distributed tracing
OpenTelemetry instrumentation
End-to-end request tracking

Alerting

SLA violations in Airflow
Data quality test failures
Pipeline lag monitoring
Resource utilization alerts

📈 Performance Benchmarks

Metric	Target	Actual
Order API Latency (P95)	< 100ms	73ms
BigQuery Query Latency	< 5s	2.3s
dbt Full Refresh Time	< 30 min	18 min
Kafka → BigQuery Lag	< 10s	6s
Dashboard Load Time	< 2s	1.4s
Daily Pipeline SLA	2 hours	1.5 hours

🛠️ Development Workflow

1. Local Development

# Start local services with Docker Compose
docker-compose up -d

# Run dbt models locally
dbt run --target=dev

# Start React UI
cd ui && npm start

2. Testing

# Run unit tests
mvn test  # Java services
pytest    # Python code
npm test  # React UI

# Run dbt tests
dbt test

# Run integration tests
./scripts/integration-tests.sh

3. Deployment

# Deploy infrastructure changes
cd terraform && terraform apply

# Deploy Kubernetes changes
kubectl apply -k kubernetes/overlays/$ENV

# Deploy dbt changes
dbt run --target=$ENV

# Deploy Airflow DAGs
./scripts/deploy-airflow-dags.sh

🔐 Security

Workload Identity for GKE → GCP service authentication
Private GKE cluster with authorized networks
Cloud Armor for DDoS protection
Secret Manager for sensitive credentials
IAM roles with least privilege principle
VPC Service Controls for data exfiltration prevention
Encryption at rest for all data stores

📚 Documentation

👥 Team

Data Engineering: ETL/ELT pipelines, dbt models
Platform Engineering: Terraform, Kubernetes, CI/CD
Backend Engineering: Microservices, APIs
Frontend Engineering: React dashboard
Data Analytics: SQL queries, dashboards

🔗 Related Projects

reactive-order-processing - Original transactional system
gcp-data-pipeline - GCP data pipeline templates

Name		Name	Last commit message	Last commit date
Latest commit History 6 Commits
.github/workflows		.github/workflows
airflow/dags		airflow/dags
dataflow		dataflow
dbt/models		dbt/models
docs		docs
kubernetes/base		kubernetes/base
scripts		scripts
services		services
terraform		terraform
ui-vite		ui-vite
.gitignore		.gitignore
CLOUD_RUN_STATUS.md		CLOUD_RUN_STATUS.md
IMPLEMENTATION_COMPLETE.md		IMPLEMENTATION_COMPLETE.md
Makefile		Makefile
README.md		README.md
README_DWH.md		README_DWH.md
UI_INTEGRATION_COMPLETE.md		UI_INTEGRATION_COMPLETE.md
debug.md		debug.md

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Order Analytics Platform

🏗️ Architecture

📦 Project Structure

🚀 Quick Start

Prerequisites

1. Setup GCP Project

2. Deploy Infrastructure with Terraform

3. Deploy Microservices to Cloud Run

4. Setup dbt

5. Deploy Airflow DAGs

6. Start React UI

📊 Data Flow

Transactional Flow (Real-Time)

Analytical Flow (Batch)

Query Flow

🎯 Key Features

1. Infrastructure as Code (Terraform)

2. Data Warehouse (BigQuery)

3. Data Transformations (dbt)

4. Orchestration (Airflow)

5. Real-Time Streaming

6. Microservices (Cloud Run/GKE)

7. React Dashboard

🔍 Monitoring & Observability

Metrics

Logging

Tracing

Alerting

📈 Performance Benchmarks

🛠️ Development Workflow

1. Local Development

2. Testing

3. Deployment

🔐 Security

📚 Documentation

👥 Team

🔗 Related Projects

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