This is a marketing analytics dashboard for audience segmentation and multi-channel ROI analysis.

Originally built as an infrastructure to solve customer marketing campaign data problems, it's now used as an easily extensible prototype in my various dashboarding work.

Here is the Superset prototype alternative w/o ETL, featuring more configurations and deployment options.

Features:

• Data Storage & ETL: PostgreSQL with Apache Airflow for ETL processing
• Advanced Metrics: CAC, CLV, ROAS, and segment-specific analytics
• Interactive Dashboard: Dark-themed Plotly Dash UI with drill-down functionality
• API Backend: RESTful endpoints for all metrics
• Containerized: Complete Docker setup for easy deployment
• CI/CD Pipeline: GitHub Actions workflow for AWS ECS/EKS deployment

The application consists of the following components:

• PostgreSQL: Main data warehouse with raw, archive, and analytics schemas
• Airflow: ETL pipeline for data processing with hourly and daily DAGs
• FastAPI: Backend API to expose metrics
• Plotly Dash: Interactive frontend dashboard
• Docker Compose: Container orchestration for local development

• Docker and Docker Compose
• Git

1. Clone the repository:

   git clone https://github.com/avrtt/dashbordina.git
   cd dashbordina

2. Start the services with Docker Compose:

   docker-compose up -d

3. Access the services:

   • Dash Dashboard: http://localhost:8050
   • Airflow UI: http://localhost:8080
   • FastAPI Docs: http://localhost:8000/docs

The database is automatically initialized with the schema and demo data when the containers start up. You can find the initialization scripts in the init-db directory.

The project includes two main DAGs:

1. marketing_etl_hourly: Runs every hour to extract, transform, and load marketing event data
2. marketing_daily_archive: Runs daily to create snapshots of the previous day's data

To trigger a DAG manually in the Airflow UI:

1. Navigate to http://localhost:8080
2. Log in with username admin and password admin
3. Find the DAG you want to run in the list
4. Click the "Trigger DAG" button

dashbordina/
├── app/
│   ├── api/           # FastAPI backend
│   ├── data/          # Data models and schemas
│   ├── etl/           # Airflow DAGs and operators
│   │   └── dags/      # Airflow DAG definitions
│   ├── frontend/      # Dash application
│   │   └── assets/    # CSS and JS assets
│   └── tests/         # Test cases
├── docker/            # Dockerfiles
├── init-db/           # Database initialization scripts
├── .github/           # GitHub Actions configuration
└── docker-compose.yml # Container orchestration

# Run all tests
docker-compose exec dash-app pytest

# Run with coverage
docker-compose exec dash-app pytest --cov=app

# Run specific test category
docker-compose exec dash-app pytest app/tests/etl/

To add authentication to the dashboard:

Modify app/frontend/index.py to include Dash authentication:

from dash_auth import BasicAuth

# Define valid username/password pairs
VALID_USERNAME_PASSWORD_PAIRS = {
    'admin': 'admin',
    'user': 'password'
}

# Add authentication to the app
auth = BasicAuth(app, VALID_USERNAME_PASSWORD_PAIRS)

For the API, add OAuth or JWT authentication in app/api/main.py.

The project includes a GitHub Actions workflow for CI/CD:

1. Testing: Runs pytest with coverage reporting
2. Building: Builds Docker images for each component
3. Deployment: Deploys to AWS ECS/EKS (configuration required)

To configure AWS deployment:

1. Add the following GitHub secrets:

   • AWS_ACCESS_KEY_ID
   • AWS_SECRET_ACCESS_KEY
   • AWS_REGION

2. Customize the deployment steps in .github/workflows/cicd.yml for your AWS infrastructure.

MIT