1. Change occurs in Jira (issue updated)
   ↓
2. Jira sends HTTP POST to Impulsum webhook endpoint
   ↓  
3. Impulsum validates webhook signature (security)
   ↓
4. Change processed y stored in real-time
   ↓
5. AI analysis triggered if needed
   ↓
6. Dashboard y notifications updated (<5 seconds)

Jira webhooks:
├── Issue created, updated, deleted
├── Issue transitioned (status changes)
├── Issue commented
├── Sprint started, completed
├── Project updated
└── User assigned/unassigned

Slack webhooks (Beta):
├── Message posted in monitored channels
├── User status changes
├── Channel created/archived
└── App mentions

GitHub webhooks (Coming):
├── Push events
├── Pull request events
├── Issue events
└── Release events

Webhook security layers:
├── HMAC SHA256 signature validation
├── IP whitelist validation
├── Timestamp validation (prevent replay attacks)
├── Rate limiting (prevent abuse)
└── SSL/TLS encryption (all webhook traffic)

Adaptive polling frequency:
├── Active projects: Every 5 minutes
├── Recently updated: Every 15 minutes  
├── Stable projects: Every hour
├── Archived projects: Daily
└── Error recovery: Exponential backoff

Optimization techniques:
├── Delta sync: Only fetch changes since last sync
├── Batch processing: Group multiple updates
├── Rate limit respect: Honor API limits
├── Off-peak scheduling: Heavy syncs during low usage
└── Conditional requests: Use ETags/If-Modified-Since

API call optimizestion:
├── Incremental sync: timestamp-based change detection
├── Field filtering: Only fetch fields we need
├── Pagination: Handle large datasets efficiently  
├── Compression: Reduce bandwidth usage
└── Caching: Avoid redundant API calls

Performance monitoring:
├── API response times
├── Rate limit utilization
├── Error rates y patterns
├── Data freshness metrics
└── User experience impact

Data processing flow:
1. Raw data ingested (webhook o polling)
   ↓
2. Data normalization y validation
   ↓
3. Context enrichment (add related data)
   ↓
4. AI analysis triggers:
   ├── Pattern recognition
   ├── Anomaly detection  
   ├── Trend analysis
   └── Risk assessment
   ↓
5. Insights generated y stored
   ↓
6. Notifications y alerts triggered
   ↓
7. Dashboard updates pushed to users

Immediate processing (<30 seconds):
├── Status change analysis
├── Workload impact assessment
├── Blocker detection
├── Timeline impact calculation
└── Team notification triggers

Batch processing (hourly):
├── Velocity trend analysis
├── Team performance metrics
├── Cross-project correlation
├── Predictive modeling updates
└── Historical pattern recognition

Deep analysis (daily):
├── Portfolio health assessment
├── Resource optimizestion recommendations
├── Long-term trend forecasting
├── Team development insights
└── Process improvement suggestions

Context-aware model routing:
├── Simple queries → Fast language model
├── Complex analysis → Advanced reasoning model
├── Data visualization → Specialized analytics model
├── Forecasting → Time series prediction model
└── Cross-tool correlation → Multi-modal analysis model

Stage 1: Ingestion
├── Webhook receivers
├── Polling schedulers
├── API rate limiters
├── Data validators
└── Error handlers

Stage 2: Transformation  
├── Schema normalization
├── Data enrichment
├── Relationship mapping
├── Quality validation
└── Duplicate detection

Stage 3: Storage
├── Real-time data store (Redis)
├── Analytical data warehouse (PostgreSQL)
├── Search index (Elasticsearch)
├── File storage (S3)
└── Cache layers (Multiple tiers)

Stage 4: Processing
├── Stream processing (real-time)
├── Batch processing (scheduled)
├── AI model inference
├── Analytics computation
└── Alert generation

Stage 5: Delivery
├── API responses
├── Dashboard updates
├── Push notifications
├── Email reports
└── Webhook deliveries

ACID compliance:
├── Atomicity: All-or-nothing data updates
├── Consistency: Data integrity maintained
├── Isolation: Concurrent operations don't interfere
└── Durability: Data persists through failures

Eventual consistency:
├── Cross-system sync may have brief delays
├── Conflict resolution for simultaneous updates
├── Reconciliation processes for data drift
└── Audit trails for change tracking

Latency targets:
├── Webhook processing: <5 seconds
├── Simple queries: <2 seconds
├── Complex analysis: <30 seconds
├── Batch reports: <5 minutes
└── Historical analysis: <2 hours

Throughput capacity:
├── Webhooks: 10,000/minute
├── API requests: 1,000/minute per user
├── Concurrent users: 10,000+
├── Data ingestion: 1GB/hour
└── Query processing: 100,000/hour

Integration setup process:
1. User clicks "Connect Jira" in Impulsum
   ↓
2. Redirect to Jira OAuth authorization
   ↓
3. User authorizes Impulsum access
   ↓
4. Jira returns authorization code
   ↓
5. Impulsum exchanges code for access token
   ↓
6. Access token stored encrypted
   ↓
7. Token used for API calls (never stored in plain text)

Security measures:
├── Tokens encrypted at rest (AES-256)
├── Tokens encrypted in transit (TLS 1.3)
├── Automatic token refresh before expiration
├── Immediate revocation on user disconnect
├── Scope limitation (only requested permissions)
└── Regular token rotation (security best practice)

Token storage:
├── Never logged o displayed
├── Separate encryption keys per customer
├── Hardware security modules (HSMs) for key management
├── Zero-knowledge architecture where possible
└── Audit trail for all token operations

Principle of least privilege:
├── Only access data user can already see
├── Respect tool-native permission systems
├── No elevation of privileges
├── Project-level access control
└── User-level data isolation

Permission validation:
├── Real-time permission checks
├── Cached permissions with TTL
├── Permission change detection
├── Automatic access revocation
└── Audit logging of permission usage

Data at rest:
├── AES-256 encryption for all stored data
├── Separate encryption keys per customer
├── Key rotation every 90 days
├── Hardware security modules (HSMs)
└── Zero-knowledge architecture for sensitive data

Data in transit:
├── TLS 1.3 for all connections
├── Certificate pinning for mobile apps
├── HSTS headers for web applications
├── Perfect forward secrecy
└── End-to-end encryption for sensitive operations

Collection principles:
├── Only collect data necessary for functionality
├── Automatic deletion of unnecessary data
├── Configurable retention policies
├── User control over data collection
└── Transparent data usage policies

Processing limitations:
├── Purpose limitation (only use for stated purpose)
├── Data anonymization where possible
├── Aggregation instead of individual tracking
├── Opt-out mechanisms for analytics
└── Clear consent management

Geographic controls:
├── Data stored in user's preferred region
├── Cross-border transfer controls
├── GDPR compliance for EU customers
├── Data sovereignty respect
└── Local law compliance

Backup y recovery:
├── Encrypted backups in multiple regions
├── Point-in-time recovery capabilities
├── Disaster recovery procedures
├── Business continuity planning
└── Regular recovery testing

Network architecture:
├── VPC isolation for customer data
├── Private subnets for sensitive operations
├── Network access control lists (NACLs)
├── Security groups for fine-grained control
└── DDoS protection y mitigation

API security:
├── Rate limiting per user y endpoint
├── Request validation y sanitization
├── SQL injection prevention
├── XSS protection
├── CSRF token validation
└── API versioning y deprecation policies

Security monitoring:
├── Real-time threat detection
├── Anomaly detection for unusual access patterns
├── Failed login attempt monitoring
├── Suspicious activity alerting
└── Automated incident response

Logging y auditing:
├── Comprehensive audit trails
├── Immutable log storage
├── Log analysis y correlation
├── Compliance reporting
└── Forensic investigation capabilities

Response procedures:
├── 24/7 security operations center
├── Automated threat response
├── Incident escalation procedures
├── Customer notification protocols
└── Post-incident analysis y improvement

Recovery procedures:
├── Backup restoration processes
├── Service continuity planning
├── Communication protocols
├── Lessons learned integration
└── Preventive measure implementation

GDPR (EU):
├── Lawful basis for processing
├── Data subject rights implementation
├── Privacy by design principles
├── Data protection impact assessments
└── Data protection officer oversight

CCPA (California):
├── Consumer rights implementation
├── Data sale opt-out mechanisms
├── Transparency in data usage
├── Non-discrimination policies
└── Verified deletion procedures

SOC 2 Type II:
├── Security controls implementation
├── Availability y processing integrity
├── Confidentiality measures
├── Privacy protection procedures
└── Regular third-party audits

ISO 27001:
├── Information security management system
├── Risk assessment y treatment
├── Security policy implementation
├── Continuous improvement processes
└── Regular certification audits

NIST Framework:
├── Identify: Asset y risk management
├── Protect: Safeguards implementation
├── Detect: Anomaly y event detection
├── Respond: Incident response procedures
└── Recover: Recovery planning y implementation

Ongoing compliance:
├── Regular security assessments
├── Penetration testing (quarterly)
├── Vulnerability scanning (weekly)
├── Compliance audits (annual)
└── Staff security training (ongoing)

Documentation y reporting:
├── Policy documentation maintenance
├── Procedure documentation updates
├── Compliance reporting automation
├── Audit trail preservation
└── Incident documentation

Layer 1: Browser cache (user device)
├── Static assets: 24 hours
├── API responses: 5 minutes
├── User preferences: 1 hour
└── Dashboard data: 2 minutes

Layer 2: CDN cache (global edge)
├── Static content: 7 days
├── API responses: 1 minute
├── Computed results: 15 minutes
└── Geographic optimizestion

Layer 3: Application cache (Redis)
├── Session data: 24 hours
├── Query results: 10 minutes
├── AI model outputs: 1 hour
└── Integration data: 5 minutes

Layer 4: Database cache (PostgreSQL)
├── Query plan cache
├── Buffer pool optimizestion
├── Index caching
└── Connection pooling

Smart invalidation strategies:
├── Time-based expiration (TTL)
├── Event-driven invalidation (webhooks)
├── Dependency-based invalidation
├── Manual cache busting (admin tools)
└── Probabilistic early expiration

Cache warming:
├── Predictive pre-loading
├── Popular query pre-computation
├── User behavior prediction
├── Scheduled cache refresh
└── Background data preparation

Cache hit rates:
├── Browser cache: 85%+ hit rate
├── CDN cache: 90%+ hit rate
├── Application cache: 95%+ hit rate
├── Database cache: 98%+ hit rate
└── Overall latency reduction: 70%

Response time improvements:
├── Cached API calls: <100ms
├── Cached dashboard loads: <500ms
├── Cached AI responses: <1s
├── Cache miss penalty: <2s additional
└── Cache rebuild time: <30s

Multi-database strategy:
├── PostgreSQL: Transactional data, complex queries
├── Redis: Session data, real-time cache
├── Elasticsearch: Search, analytics, logging
├── InfluxDB: Time series data, metrics
└── S3: File storage, backups, archives

Partitioning strategy:
├── Horizontal partitioning by customer
├── Vertical partitioning by data type
├── Time-based partitioning for historical data
├── Geographic partitioning for compliance
└── Automatic partition management

Performance techniques:
├── Index optimizestion (B-tree, GIN, GIST)
├── Query plan analysis y optimizestion
├── Materialized views for complex aggregations
├── Partial indexes for filtered queries
└── Covering indexes for read-heavy workloads

Connection management:
├── Connection pooling (PgBouncer)
├── Read replicas for query distribution
├── Load balancing across replicas
├── Automatic failover mechanisms
└── Connection limit management

Automated data management:
├── Hot data: SSD storage, frequent access
├── Warm data: Standard storage, occasional access
├── Cold data: Archive storage, rare access
├── Automatic tiering based on usage patterns
└── Configurable retention policies

Backup y recovery:
├── Continuous backup (point-in-time recovery)
├── Cross-region backup replication
├── Automated backup testing
├── Recovery time objectives (RTO): <1 hour
└── Recovery point objectives (RPO): <5 minutes

Response time targets:
├── Simple queries: <200ms (95th percentile)
├── Complex analysis: <2s (95th percentile)
├── Real-time updates: <100ms (99th percentile)
├── Bulk operations: <30s (regardless of size)
└── Error responses: <50ms (immediate feedback)

Optimization techniques:
├── GraphQL for efficient data fetching
├── Response compression (gzip, brotli)
├── HTTP/2 for multiplexed connections
├── Async processing for long operations
└── Streaming responses for large datasets

Intelligent rate limiting:
├── Per-user limits based on plan
├── Per-endpoint limits for resource protection
├── Burst allowance for occasional spikes
├── Graceful degradation under load
└── Priority queuing for critical operations

Throttling strategies:
├── Token bucket algorithm
├── Sliding window counters
├── Adaptive limits based on system load
├── User feedback for limit approaching
└── Automatic limit increases for good actors

Performance metrics:
├── Response time percentiles (50th, 95th, 99th)
├── Throughput (requests per second)
├── Error rates by endpoint y user
├── Resource utilization (CPU, memory, I/O)
└── User experience metrics (satisfaction, abandonment)

Alerting y response:
├── Real-time performance monitoring
├── Automatic scaling triggers
├── Performance degradation alerts
├── Capacity planning recommendations
└── Proactive optimizestion suggestions

Multi-region deployment:
├── Primary regions: US East, EU West, Asia Pacific
├── Edge locations: 50+ cities worldwide
├── Data residency compliance
├── Automatic failover between regions
└── Load balancing based on user location

Content delivery:
├── Static assets via global CDN
├── API responses cached at edge
├── Dynamic content optimizestion
├── Image y file optimizestion
└── Bandwidth optimizestion

Geographic performance:
├── Sub-100ms response times globally
├── Intelligent routing to nearest endpoint
├── Regional data caching
├── Predictive content pre-positioning
└── Network path optimizestion

Connection optimizestion:
├── HTTP/2 y HTTP/3 support
├── Connection keep-alive optimizestion
├── TCP optimizestion for long distances
├── Compression for bandwidth-limited connections
└── Mobile-specific optimizestions

Business continuity:
├── Multi-region active-active setup
├── Automatic failover (RTO: <5 minutes)
├── Data synchronization across regions
├── Regular disaster recovery testing
└── Transparent user experience during failover

Monitoring y alerting:
├── Global health monitoring
├── Regional performance tracking
├── Automatic incident detection
├── Customer communication during outages
└── Post-incident analysis y improvement