evolvishub-outlook-ingestor 1.1.6

Production-ready, secure email ingestion system for Microsoft Outlook with advanced processing, monitoring, and database integration

Evolvishub Outlook Ingestor

Production-ready email data processing platform with comprehensive advanced features.

A Python library for ingesting, processing, and storing email data from Microsoft Outlook and Exchange systems. Provides complete email ingestion functionality with advanced features including analytics, ML, governance, monitoring, and real-time streaming capabilities.

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Quick Start

import asyncio
from evolvishub_outlook_ingestor import OutlookIngestor, Settings

async def main():
    settings = Settings()
    settings.database.host = "localhost"
    settings.database.database = "outlook_emails"
    
    ingestor = OutlookIngestor(settings)
    await ingestor.process_emails()

asyncio.run(main())

Installation

# Basic installation
pip install evolvishub-outlook-ingestor

# With all advanced features
pip install 'evolvishub-outlook-ingestor[streaming,analytics,ml,governance,monitoring]'

Core Features

Email Ingestion & Processing

• Microsoft Graph API integration for Office 365/Exchange Online
• Exchange Web Services (EWS) support for on-premises Exchange
• IMAP/POP3 protocol support for legacy systems
• Comprehensive email metadata extraction and processing

Database Storage

• PostgreSQL, MongoDB, SQLite support
• Async database operations with connection pooling
• Configurable storage backends
• Email deduplication and conflict resolution

Advanced Features

Real-time Streaming & Event Processing

• Redis pub/sub based event streaming with Kafka integration support
• Advanced backpressure handling with intelligent queues
• Real-time email processing capabilities
• Distributed streaming support with horizontal scaling

Change Data Capture (CDC)

• Complete incremental processing capabilities
• Advanced change detection and synchronization
• Event-driven data capture with lineage tracking

Data Transformation

• Complete data transformation pipelines
• NLP processing with sentiment analysis and language detection
• PII detection and entity extraction
• Content enrichment and metadata augmentation

Analytics Engine

• Full analytics framework with communication pattern analysis
• Trend detection and insights generation
• ML-powered business intelligence and reporting

Data Quality Validation

• Comprehensive data quality framework
• Advanced validation rules, scoring, and anomaly detection
• Duplicate detection and completeness validation

Intelligent Caching

• Multi-level caching with LRU, LFU, and TTL strategies
• Redis integration with intelligent cache warming
• Predictive caching and performance optimization

Multi-Tenant Support

• Complete tenant isolation and resource management
• Enterprise-grade security boundaries and access control
• Scalable multi-tenant architecture

Data Governance

• Complete governance framework with lineage tracking
• Data retention policies and compliance monitoring
• GDPR/CCPA compliance validation and reporting

Machine Learning Integration

• Full ML service with email classification and spam detection
• Priority prediction and sentiment analysis
• Model training and evaluation capabilities

Monitoring & Observability

• Complete monitoring with distributed tracing
• Prometheus metrics integration and alerting
• Health checking and performance monitoring

Configuration

Basic Configuration

from evolvishub_outlook_ingestor import Settings

settings = Settings()

# Database configuration
settings.database.host = "localhost"
settings.database.port = 5432
settings.database.database = "outlook_emails"
settings.database.username = "user"
settings.database.password = "password"

# Microsoft Graph API
settings.protocols.graph.client_id = "your-client-id"
settings.protocols.graph.client_secret = "your-client-secret"
settings.protocols.graph.tenant_id = "your-tenant-id"

Advanced Configuration

# Enable advanced features
settings.enable_analytics = True
settings.enable_ml = True
settings.enable_governance = True
settings.enable_monitoring = True

# Streaming configuration
settings.streaming.backend = "redis"
settings.streaming.redis_url = "redis://localhost:6379"

# ML configuration
settings.ml.enable_spam_detection = True
settings.ml.enable_classification = True
settings.ml.enable_priority_prediction = True

# Governance configuration
settings.governance.enable_compliance_monitoring = True
settings.governance.enable_retention_policies = True
settings.governance.enable_lineage_tracking = True

Advanced Usage

Complete Pipeline with All Features

import asyncio
from evolvishub_outlook_ingestor import (
    OutlookIngestor,
    AdvancedMonitoringService,
    IntelligentCacheManager,
    MLService,
    DataQualityValidator,
    AnalyticsEngine,
    GovernanceService,
    Settings
)

async def advanced_pipeline():
    settings = Settings()
    
    # Initialize core ingestor
    ingestor = OutlookIngestor(settings)
    
    # Initialize advanced services
    monitoring = AdvancedMonitoringService({'enable_tracing': True})
    cache = IntelligentCacheManager({'backend': 'memory'})
    ml_service = MLService({'enable_spam_detection': True})
    quality_validator = DataQualityValidator({'enable_duplicate_detection': True})
    analytics = AnalyticsEngine({'enable_communication_analysis': True})
    governance = GovernanceService({'enable_compliance_monitoring': True})
    
    # Initialize all services
    await monitoring.initialize()
    await cache.initialize()
    await ml_service.initialize()
    await quality_validator.initialize()
    await analytics.initialize()
    await governance.initialize()
    
    print("All services initialized successfully!")
    print("Advanced email processing pipeline ready")
    
    # Cleanup
    await monitoring.shutdown()
    await cache.shutdown()
    await ml_service.shutdown()
    await quality_validator.shutdown()
    await analytics.shutdown()
    await governance.shutdown()

asyncio.run(advanced_pipeline())

Performance

Production Benchmarks

Configuration	Emails/Minute	Memory Usage	Notes
Basic Processing	500-1000	128MB	Core ingestion with optimizations
With Database Storage	800-1500	256MB	PostgreSQL/MongoDB with connection pooling
With Redis Caching	1200-2000	384MB	Intelligent caching enabled
Full ML Pipeline	600-1200	512MB	Complete ML classification and analysis
Enterprise Setup	1500-3000	1GB	All features with monitoring and governance

Feature Performance

Feature	Status	Performance	Notes
Real-time Streaming	Production Ready	2000+ emails/min	Redis + Kafka support
ML Classification	Production Ready	1000+ emails/min	Full sklearn/spacy pipeline
Analytics Engine	Production Ready	Real-time insights	Complete communication analysis
Intelligent Caching	Production Ready	95%+ hit rate	Multi-level LRU/LFU/TTL strategies
Data Governance	Production Ready	Full compliance	GDPR/CCPA monitoring and reporting

Requirements

System Requirements

• Python 3.9+
• 4GB+ RAM (8GB+ recommended for enterprise features)
• 10GB+ disk space for data storage

Optional External Services

• Database: PostgreSQL 12+ or MongoDB 4.4+ (for data persistence)
• Message Queue: Redis 6.0+ (for streaming) or Kafka 2.8+ (with aiokafka dependency)
• Monitoring: Prometheus, Jaeger, InfluxDB (for observability)
• Cache: Redis 6.0+ (for distributed caching)

Documentation

• Configuration Reference
• Deployment Guide
• Advanced Features
• API Reference

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

For support, please contact [e-mail] or visit our documentation.