Implement complete streaming architecture with Airflow 3, Kafka, Redis, PostgreSQL, Celery, and Flower

[Copilot]

This PR implements a comprehensive streaming data pipeline architecture using modern technologies for event-driven data processing and workflow orchestration.

Architecture Overview

The implementation provides a production-ready streaming pipeline with the following components:

• Apache Airflow 3 - Workflow orchestration with CeleryExecutor for distributed processing
• Apache Kafka - High-throughput event streaming platform with Zookeeper coordination
• Redis - In-memory caching and Celery message broker
• PostgreSQL - Primary database with optimized streaming schema
• Celery + Flower - Distributed task processing with monitoring
• Management Tools - pgAdmin, Kafka UI, and development utilities

Key Features

🏗️ Infrastructure

• Complete Docker Compose setup with 11+ services
• Modern Docker Compose v2 syntax with health checks
• Development environment with Jupyter, Grafana, Prometheus
• One-command startup with automated service verification

📊 Data Pipeline

• End-to-end streaming pipeline demonstration
• Kafka → Redis → PostgreSQL data flow
• Custom Airflow operators for Kafka integration
• Automated data aggregation and cleanup procedures

🔧 Developer Experience

• Interactive Jupyter notebook with streaming analysis examples
• Comprehensive documentation and verification guide
• Automated setup scripts with health checks
• Hot-reloading for development workflow

Files Added

Core Infrastructure:

• docker-compose.yaml - Main services configuration
• docker-compose.dev.yaml - Development environment extensions
• Dockerfile - Custom Airflow image with dependencies
• .env - Environment variables and configuration

Application Code:

• dags/streaming_pipeline_demo.py - Sample end-to-end streaming DAG
• plugins/kafka_operators.py - Custom Kafka operators for Airflow
• scripts/kafka_utils.py - Kafka management utilities
• scripts/celery_config.py - Celery configuration

Database & Setup:

• init-scripts/01-init-streaming-schema.sql - PostgreSQL schema initialization
• pgadmin/servers.json - Pre-configured database connections
• scripts/start.sh - Automated startup script
• scripts/cleanup.sh - Development cleanup utilities

Documentation:

• README.md - Comprehensive architecture documentation
• SETUP_VERIFICATION.md - Step-by-step verification guide
• notebooks/streaming_analysis.ipynb - Interactive analysis examples

Getting Started

# Start the complete pipeline
./scripts/start.sh

# Access services
# Airflow: http://localhost:8080 (airflow/airflow)
# Flower: http://localhost:5555
# pgAdmin: http://localhost:5050 (admin@example.com/admin123)
# Kafka UI: http://localhost:8090

Demo Pipeline

The included sample DAG demonstrates a complete streaming workflow:

1. Data Generation - Creates sample streaming events
2. Kafka Production - Publishes events to Kafka topics
3. Stream Processing - Consumes and processes events
4. Redis Caching - Caches processed data for fast access
5. Data Aggregation - Stores aggregated results in PostgreSQL
6. Cleanup - Maintains system performance

This architecture provides a solid foundation for building production streaming applications with modern DevOps practices, comprehensive monitoring, and scalable distributed processing capabilities.

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[c2012mato]

we should utilize the new airflow 3.0.0 i.e use airflow-apiserver completely as our webui and backend api communication. Also, let's utilize airflow.sdk dags, tasks, assests if needed and avoid using operators that is already optimized in airflow 3.0.0

[c2012mato]

looks good