DataFlow - Data Pipeline Platform

A growing ETL platform for managing data workflows. Still early stage but getting there!

DataFlow is a comprehensive, scalable data pipeline platform designed for modern enterprises. It supports both real-time streaming and batch ETL workloads with advanced data quality monitoring, lineage tracking, and operational visibility.

🚀 Key Features

Multi-Source Data Ingestion

• 20+ Built-in Connectors: PostgreSQL, MySQL, MongoDB, Salesforce, REST APIs, Kafka, S3, HDFS
• Pluggable Architecture: Easy to add custom connectors
• Schema Evolution: Automatic handling of schema changes
• Incremental Loading: Optimized data synchronization

Real-Time & Batch Processing

• Streaming ETL: Apache Kafka-based real-time processing
• Batch Processing: Apache Spark integration for large-scale transformations
• Hybrid Workloads: Seamless combination of streaming and batch
• Auto-scaling: Kubernetes-native horizontal scaling

Data Quality & Governance

• Built-in Validators: 50+ pre-built data quality rules
• Custom Validators: Python-based extensible validation framework
• Data Profiling: Automatic statistics and anomaly detection
• Data Lineage: End-to-end tracking of data transformations
• GDPR Compliance: Built-in PII detection and handling

Enterprise-Grade Operations

• Web Dashboard: React-based management interface
• RESTful APIs: Complete programmatic control
• Monitoring & Alerting: Prometheus metrics with Grafana dashboards
• Multi-tenancy: Isolated environments for different teams
• Role-based Access: Fine-grained security controls

🏗️ Architecture

DataFlow follows a microservices architecture designed for cloud-native deployments:

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Data Sources  │    │   Processing    │    │   Destinations  │
│                 │    │                 │    │                 │
│ • Databases     │────│ • Stream Proc   │────│ • Data Lake     │
│ • APIs          │    │ • Batch Proc    │    │ • Data Warehouse│  
│ • Files         │    │ • Validators    │    │ • Analytics     │
│ • Streams       │    │ • Transformers  │    │ • ML Platforms  │
└─────────────────┘    └─────────────────┘    └─────────────────┘
         │                       │                       │
         └───────────────────────┼───────────────────────┘
                                 │
                    ┌─────────────────┐
                    │   DataFlow      │
                    │   Control Plane │
                    │                 │
                    │ • Orchestration │
                    │ • Monitoring    │
                    │ • Metadata      │
                    │ • Security      │
                    └─────────────────┘

🛠️ Technology Stack

• Runtime: Python 3.7+, Apache Spark 2.4+, Apache Kafka 2.2+
• Storage: PostgreSQL, Redis, Apache Parquet
• Container Platform: Docker, Kubernetes
• Monitoring: Prometheus, Grafana, ELK Stack
• Web Framework: Flask, React, TypeScript

📦 Quick Start

Prerequisites

• Docker 18.09+
• Kubernetes 1.14+ (for production)
• Python 3.7+ (for development)

Installation

1. Deploy with Docker Compose (Development):

git clone https://github.com/your-org/dataflow.git
cd dataflow
docker-compose up -d

2. Deploy on Kubernetes (Production):

helm repo add dataflow https://charts.dataflow.io
helm install dataflow dataflow/dataflow-platform

3. Local Development Setup:

python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
python -m dataflow.cli init --dev

Creating Your First Pipeline

from dataflow import Pipeline, PostgreSQLSource, S3Sink, Validator

pipeline = Pipeline("user_events_etl")

# Source: PostgreSQL database
source = PostgreSQLSource(
    connection_string="postgresql://user:pass@localhost/prod",
    query="SELECT * FROM user_events WHERE created_at > '{last_run}'"
)

# Validation: Ensure data quality
validator = Validator()
validator.add_rule("user_id", "not_null")
validator.add_rule("event_type", "in", ["click", "view", "purchase"])
validator.add_rule("timestamp", "datetime_format", "%Y-%m-%d %H:%M:%S")

# Sink: Amazon S3 data lake
sink = S3Sink(
    bucket="company-data-lake",
    prefix="events/year={year}/month={month}/day={day}",
    format="parquet",
    compression="snappy"
)

# Build pipeline
pipeline.source(source)
pipeline.validate(validator)
pipeline.transform("events_transformer.py")
pipeline.sink(sink)

# Schedule for every hour
pipeline.schedule("0 * * * *")
pipeline.save()

📊 Use Cases

Real-Time Analytics Pipeline

from dataflow.streaming import KafkaSource, StreamProcessor

processor = StreamProcessor("real_time_analytics")
processor.source(KafkaSource("user_events"))
processor.window(size="5m", slide="1m")
processor.aggregate(["user_id"], ["count", "sum(revenue)"])
processor.sink(RedisCache("analytics_cache"))

Data Quality Monitoring

from dataflow.quality import DataProfiler, QualityMonitor

monitor = QualityMonitor("daily_quality_check")
monitor.profile_all_tables()
monitor.detect_anomalies(sensitivity=0.95)
monitor.alert_on_failure(slack_channel="#data-ops")

🔧 Configuration

DataFlow uses YAML-based configuration:

# dataflow.yml
engine:
  parallelism: 4
  checkpoint_interval: "5m"
  
storage:
  metadata_db: "postgresql://localhost/dataflow_meta"
  cache: "redis://localhost:6379"

monitoring:
  prometheus:
    enabled: true
    port: 9090
  logging:
    level: INFO
    format: json

security:
  authentication: oauth2
  encryption: aes256

🚦 Production Deployment

Scaling Guidelines

• Small deployment: 2-4 worker nodes, handles ~1GB/hour
• Medium deployment: 8-16 worker nodes, handles ~50GB/hour
• Large deployment: 32+ worker nodes, handles ~500GB/hour

High Availability Setup

# kubernetes/values.yml
replicaCount: 3
autoscaling:
  enabled: true
  minReplicas: 3
  maxReplicas: 20
  targetCPUUtilizationPercentage: 70

📈 Performance Benchmarks

Workload Type	Throughput	Latency	Resource Usage
Batch ETL	2GB/min	N/A	4 CPU, 8GB RAM
Streaming	100k msg/s	<100ms	2 CPU, 4GB RAM
Data Quality	1M rows/s	<50ms	1 CPU, 2GB RAM

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

make dev-setup
make test
make lint

Running Tests

# Unit tests
pytest tests/unit/

# Integration tests  
pytest tests/integration/

# End-to-end tests
pytest tests/e2e/

📄 License

DataFlow is licensed under the Apache License 2.0. See LICENSE for details.

🆘 Support

• 📖 Documentation
• 💬 Community Slack
• 🐛 Issue Tracker
• 📧 Enterprise Support

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Made with ❤️ by the DataFlow Team