Events Catalog - Audit Trail Platform (ATP)¶

Events as first-class citizens — ATP's event catalog documents all domain events, integration events, and message contracts with schemas, versioning, and publishing patterns for complete event-driven transparency.

📋 Documentation Generation Plan¶

This document will be generated in 10 cycles. Current progress:

Cycle	Topics	Estimated Lines	Status
Cycle 1	Event-Driven Architecture & Event Types (1-2)	~3,000	⏳ Not Started
Cycle 2	Domain Events Catalog (3-4)	~3,500	⏳ Not Started
Cycle 3	Integration Events & Message Contracts (5-6)	~3,000	⏳ Not Started
Cycle 4	Event Schemas & Versioning (7-8)	~2,500	⏳ Not Started
Cycle 5	Event Publishing & Handling (9-10)	~3,000	⏳ Not Started
Cycle 6	Event Sourcing & CQRS (11-12)	~3,000	⏳ Not Started
Cycle 7	Event Processing & Projections (13-14)	~2,500	⏳ Not Started
Cycle 8	Event Reliability & Patterns (15-16)	~2,500	⏳ Not Started
Cycle 9	Event Observability & Compliance (17-18)	~2,500	⏳ Not Started
Cycle 10	Event Testing & Documentation (19-20)	~2,500	⏳ Not Started

Total Estimated Lines: ~28,000

Purpose & Scope¶

This document provides a comprehensive catalog of all events in the ConnectSoft Audit Trail Platform (ATP), documenting domain events, integration events, and message contracts with complete schemas, versioning strategies, publishing patterns, and consumption rules to support ATP's event-driven architecture and ensure consistency, traceability, and compliance across all system interactions.

Key Event Architecture Principles - Event-Driven Architecture (EDA): Events as core communication mechanism, choreography over orchestration - Event Sourcing: Events as source of truth, immutable event store, state reconstruction from events - CQRS (Command Query Responsibility Segregation): Separate write models (commands/events) from read models (queries) - Domain Events: Business-meaningful state changes within bounded contexts - Integration Events: Cross-context communication, published language - Event Versioning: Schema evolution, backward/forward compatibility - At-Least-Once Delivery: Idempotency, deduplication, event ordering - Event Observability: Tracing, monitoring, debugging event flows

What this document covers

Establish ATP's event-driven architecture with event types, patterns, and best practices
Define domain events catalog: All business-meaningful events with schemas, producers, consumers
Specify integration events catalog: Cross-context events with message contracts and routing
Detail event schemas: JSON Schema, Protocol Buffers, Avro formats with validation
Describe event versioning: Schema evolution strategies, compatibility rules, migration patterns
Outline event publishing patterns: Outbox pattern, transactional messaging, guaranteed delivery
Document event handling patterns: Idempotency, deduplication, retry, dead-letter queues
Specify event sourcing: Event store design, snapshots, projections, replay
Detail CQRS integration: Command handling, event projection, read model updates
Describe event reliability: At-least-once delivery, ordering guarantees, failure handling
Outline event observability: Distributed tracing, event monitoring, debugging tools
Document event compliance: Audit trails, event retention, GDPR considerations
Specify event testing: Contract testing, event replay testing, chaos engineering

Out of scope (referenced elsewhere)

Aggregates and entity design (see aggregates-entities.md)
REST/gRPC API contracts (see rest-apis.md)
Message broker infrastructure (Azure Service Bus) (see messaging.md)
Microservice architecture (see components.md)
Data storage and indexing (see data-model.md)
Deployment and operations (see deployment-views.md)

Readers & ownership

Backend Developers (owners): Event publishing, handling, schema design, implementation
Architects: Event-driven architecture, bounded contexts, integration patterns, CQRS design
Domain Experts: Domain event naming, semantics, business rules
Integration Engineers: Integration events, message routing, external system integration
QA/Test Engineers: Event contract testing, replay testing, chaos engineering
Operations/SRE: Event monitoring, troubleshooting, performance tuning, reliability
Compliance/Audit: Event audit trails, retention policies, compliance evidence

Artifacts produced

Events Catalog: Complete list of all domain and integration events with metadata
Event Schemas: JSON Schema, Protocol Buffers, Avro definitions for all events
Event Flow Diagrams: Sequence diagrams showing event producers, consumers, and flows
Event Versioning Guide: Schema evolution rules, compatibility matrix, migration procedures
Publishing Patterns: Outbox implementation, transactional messaging, delivery guarantees
Handling Patterns: Idempotency handlers, deduplication, retry policies, DLQ handling
Event Store Schema: Event storage structure, indexing, partitioning strategies
Projection Specifications: Read model projections from event streams
Event Monitoring Dashboards: Metrics, alerts, tracing for event flows
Contract Tests: Automated tests for event schema validation and compatibility
Event Replay Tools: Scripts and procedures for event replay and debugging
Compliance Documentation: Event audit trails, retention policies, GDPR compliance

Acceptance (done when)

All domain events are cataloged with complete schemas, producers, and consumers documented
All integration events are documented with message contracts, routing rules, and consumers
Event schemas are defined in JSON Schema, Protocol Buffers, or Avro with validation rules
Versioning strategy is documented with compatibility rules and migration procedures
Publishing patterns are implemented with outbox pattern and guaranteed delivery
Handling patterns include idempotency, deduplication, retry, and DLQ processing
Event sourcing is documented with event store design, snapshots, and replay procedures
CQRS projections are specified for all read models with update strategies
Event reliability is ensured with at-least-once delivery, ordering, and failure handling
Observability is operational with distributed tracing, monitoring, and debugging tools
Compliance requirements are met with audit trails, retention policies, and GDPR compliance
Testing includes contract tests, replay tests, and chaos engineering scenarios
Documentation complete with comprehensive examples, diagrams, code samples, and troubleshooting guides

Detailed Cycle Plan¶

CYCLE 1: Event-Driven Architecture & Event Types (~3,000 lines)¶

Topic 1: Event-Driven Architecture Fundamentals¶

What will be covered:

Event-Driven Architecture (EDA) Overview
What is EDA and why ATP uses it
Events vs requests (push vs pull)
Choreography vs orchestration
Benefits for ATP (loose coupling, scalability, audit trail)
Challenges (eventual consistency, debugging complexity)
Event-Driven Patterns in ATP
Event Notification: Notify consumers of state changes
Event-Carried State Transfer: Include full state in event
Event Sourcing: Events as source of truth
CQRS: Separate write and read models
Saga Pattern: Distributed transactions via events
Event Flow Architecture
Producer → Event Bus → Consumer pattern
ATP event flow: Ingestion → Storage → Projection → Query
Azure Service Bus as event backbone
Topic/subscription model
Event routing and filtering
Event Guarantees
At-Least-Once Delivery: Events may be delivered multiple times
Ordering Guarantees: Within partition/session
Idempotency: Consumers must handle duplicates
Eventual Consistency: Accept temporary inconsistency
Event-Driven vs Request-Driven
Comparison table (synchronous vs asynchronous)
When to use each pattern
Hybrid approaches in ATP
ATP Event Architecture Principles
Events are immutable (never modified after publishing)
Events are append-only (stored forever or per retention)
Events are the source of truth (event sourcing)
Events enable audit trails (complete history)
Events support CQRS (write/read separation)
Events enable scalability (async processing)

Code Examples: - Event base class definition - Event publisher interface - Event handler interface - Azure Service Bus integration - Event flow architecture diagram

Diagrams: - EDA overview - ATP event flow architecture - Choreography vs orchestration - Event guarantees visualization - Producer-consumer pattern

Deliverables: - EDA fundamentals guide - ATP event architecture specification - Event flow diagrams - Pattern selection guide

Topic 2: Event Types and Classification¶

What will be covered:

Domain Events
Definition: Business-meaningful state changes
Naming convention: Past tense (OrderPlaced, UserRegistered)
Bounded to single aggregate
Published after aggregate commit
Examples in ATP: AuditEventReceived, EventStreamSealed
Integration Events
Definition: Cross-bounded-context communication
Published language between contexts
Decoupled from internal domain model
Versioned and backward compatible
Examples in ATP: TenantOnboarded, EventExported
System Events
Infrastructure and operational events
Non-business events (system startup, health checks)
Monitoring and alerting triggers
Examples: ServiceStarted, HealthCheckFailed
Command vs Event
Command: Request to do something (imperative)
Event: Something that happened (past tense)
Commands can be rejected; events cannot
Commands have single recipient; events have multiple consumers
Event Naming Conventions
Format: {Aggregate}{Action} (PastTense)
Good: AuditEventReceived, TenantOnboarded, PolicyUpdated
Bad: ReceiveAuditEvent (imperative), AuditEvent (not descriptive)
Versioning: AuditEventReceivedV2 (explicit version suffix)
Event Categorization in ATP
Ingestion Events: AuditEventReceived, BatchIngested
Storage Events: EventStreamCreated, EventStreamSealed
Classification Events: EventClassified, PIIDetected
Retention Events: RetentionApplied, EventTombstoned
Export Events: ExportRequested, ExportCompleted
Policy Events: PolicyCreated, PolicyUpdated
Tenant Events: TenantOnboarded, TenantSuspended
Integrity Events: HashChainValidated, SignatureVerified
Event Metadata (Envelope)
EventId: Unique identifier (ULID)
EventType: Fully qualified event name
Timestamp: When event occurred (UTC)
CorrelationId: Request correlation
CausationId: Event that caused this event
TenantId: Multi-tenancy context
AggregateId: Which aggregate instance
AggregateType: Type of aggregate
Version: Event schema version
Event Payload
Business data specific to event
Minimal vs enriched payload strategies
Payload encryption for sensitive data
Payload size considerations

Code Examples: - Domain event base class - Integration event base class - Event metadata (envelope) - Event payload examples - Naming convention examples

Diagrams: - Event type hierarchy - Domain vs integration events - Command vs event flow - Event categorization in ATP - Event envelope structure

Deliverables: - Event type specifications - Naming convention guide - Event categorization matrix - Metadata standards - Code templates

CYCLE 2: Domain Events Catalog (~3,500 lines)¶

Topic 3: Ingestion & Storage Domain Events¶

What will be covered:

AuditEventReceived
Description: New audit event successfully ingested
Producer: Ingestion API
Consumers: Storage Service, Classification Service
Schema: EventId, TenantId, Timestamp, Actor, Action, Resource, Payload
Business rules: Must pass validation, duplicate detection
Retry policy: 3 attempts with exponential backoff
DLQ handling: After 3 failures, send to dead-letter queue
AuditEventValidationFailed
Description: Event rejected due to validation failure
Producer: Ingestion API
Consumers: Monitoring, Alerting
Schema: EventId, TenantId, ValidationErrors, OriginalPayload
BatchIngestionStarted
Description: Batch ingestion initiated
Producer: Batch Ingestion Service
Schema: BatchId, TenantId, EventCount, Source
BatchIngestionCompleted
Description: Batch ingestion finished
Producer: Batch Ingestion Service
Schema: BatchId, TenantId, SuccessCount, FailureCount, Duration
EventStreamCreated
Description: New event stream initialized
Producer: Storage Service
Consumers: Query Service, Integrity Service
Schema: StreamId, TenantId, SubjectId, StreamType
EventAddedToStream
Description: Event appended to stream
Producer: Storage Service
Schema: StreamId, EventId, SequenceNumber, PreviousHash
EventStreamSealed
Description: Stream finalized, no more events
Producer: Storage Service
Consumers: Integrity Service, Archive Service
Schema: StreamId, FinalEventCount, MerkleRoot, SealedAt
EventStreamArchived
Description: Stream moved to cold storage
Producer: Archive Service
Schema: StreamId, ArchiveLocation, ArchivedAt

For Each Event: - Complete schema definition (JSON Schema) - Producer and consumers list - Business rules and invariants - Publishing pattern (transactional outbox) - Handling pattern (idempotency key) - Retry and DLQ policies - Monitoring and alerting - Example payload (JSON)

Code Examples: - Event class definitions (C#) - JSON Schema definitions - Event publishing code - Event handler code - Idempotency implementation

Diagrams: - Ingestion event flow - Storage event flow - Event producer-consumer map - Sequence diagrams

Deliverables: - Ingestion events catalog - Storage events catalog - Event schemas (JSON Schema) - Event flow diagrams - Code implementations

Topic 4: Classification, Retention & Policy Domain Events¶

What will be covered:

EventClassified
Description: Classification applied to event
Producer: Classification Service
Consumers: Query Service, Retention Service
Schema: EventId, Classification, Confidence, ClassificationRules
PIIDetected
Description: PII found in event payload
Producer: Classification Service
Schema: EventId, PIITypes, Locations, RedactionRequired
EventRedacted
Description: PII redacted from event
Producer: Classification Service
Schema: EventId, RedactedFields, RedactionMethod
RetentionPolicyApplied
Description: Retention policy set on event
Producer: Retention Service
Schema: EventId, PolicyId, RetentionPeriod, ExpiresAt
LegalHoldApplied
Description: Legal hold placed on event
Producer: Retention Service
Schema: EventId, HoldId, HoldReason, AppliedBy
LegalHoldReleased
Description: Legal hold removed from event
Producer: Retention Service
Schema: EventId, HoldId, ReleasedBy, ReleasedAt
EventTombstoned
Description: Event soft-deleted (retention expired)
Producer: Retention Service
Schema: EventId, TombstoneReason, TombstonedAt
EventPermanentlyDeleted
Description: Event hard-deleted (GDPR right to be forgotten)
Producer: Retention Service
Schema: EventId, DeletionReason, DeletedBy
PolicyCreated
Description: New tenant policy created
Producer: Policy Service
Consumers: Classification Service, Retention Service
Schema: PolicyId, TenantId, PolicyType, Rules
PolicyUpdated
Description: Existing policy modified
Producer: Policy Service
Schema: PolicyId, Changes, UpdatedBy, Version
PolicyDeleted
Description: Policy removed
Producer: Policy Service
Schema: PolicyId, DeletedBy, DeletedAt

Complete specifications for each event (same structure as Topic 3)

Deliverables: - Classification events catalog - Retention events catalog - Policy events catalog - Event schemas - Implementation code

CYCLE 3: Integration Events & Message Contracts (~3,000 lines)¶

Topic 5: Cross-Context Integration Events¶

What will be covered:

Integration Events vs Domain Events
Domain events: Internal to bounded context
Integration events: Cross-context communication
Translation layer (anti-corruption layer)
Versioning and compatibility requirements
Tenant Integration Events
TenantOnboarded: New tenant registered
TenantConfigurationUpdated: Tenant settings changed
TenantSuspended: Tenant access suspended
TenantReactivated: Tenant access restored
TenantDeleted: Tenant permanently removed
Export Integration Events
ExportRequested: Export initiated
ExportStarted: Export processing began
ExportCompleted: Export finished successfully
ExportFailed: Export failed with errors
ExportDelivered: Export sent to destination
Integrity Integration Events
HashChainValidated: Integrity verification passed
HashChainValidationFailed: Integrity violation detected
SignatureVerified: Digital signature valid
SignatureVerificationFailed: Invalid signature
External System Integration Events
SIEMEventPublished: Event sent to SIEM
WebhookDelivered: Webhook successfully sent
WebhookDeliveryFailed: Webhook failed
Message Contract Design
Contract-first approach
Published language (shared schemas)
Backward compatibility rules
Forward compatibility considerations
Anti-corruption layer at boundaries

For Each Integration Event: - Purpose and business context - Producer context and consumer context(s) - Message contract (schema) - Routing rules - Versioning strategy - Compatibility requirements - Anti-corruption layer mapping - SLA and delivery guarantees

Code Examples: - Integration event definitions - Message contracts (JSON Schema) - Anti-corruption layer implementation - Event translation code - Routing configuration

Diagrams: - Context integration map - Integration event flows - Anti-corruption layer - Message routing

Deliverables: - Integration events catalog - Message contracts - Integration patterns guide - Anti-corruption layer specs

Topic 6: Message Routing & Delivery¶

What will be covered:

Azure Service Bus Integration
Topics and subscriptions model
Message routing rules
Subscription filters
Dead-letter queues
Event Routing Patterns
Direct Routing: Point-to-point delivery
Pub/Sub: Broadcast to multiple consumers
Content-Based Routing: Route by event properties
Topic-Based Routing: Route by event type
Message Filtering
SQL filters in Service Bus
Correlation filters
Custom properties for routing
Delivery Guarantees
At-least-once delivery
Ordered delivery within sessions
Duplicate detection
Time-to-live (TTL)
Retry Policies
Exponential backoff
Max retry attempts
Retry delay configuration
Dead-Letter Queue Handling
When events go to DLQ
DLQ monitoring and alerting
DLQ reprocessing procedures
Manual intervention triggers

Code Examples: - Service Bus topic configuration - Subscription setup with filters - Message routing rules - Retry policy configuration - DLQ processing code

Diagrams: - Message routing architecture - Pub/sub pattern - DLQ handling flow - Retry mechanism

Deliverables: - Routing configuration guide - Service Bus setup scripts - DLQ procedures - Monitoring setup

CYCLE 4: Event Schemas & Versioning (~2,500 lines)¶

Topic 7: Event Schema Design¶

What will be covered:

Schema Formats
JSON Schema: Human-readable, flexible
Protocol Buffers: Compact, strongly-typed
Apache Avro: Schema evolution, big data friendly
Format selection for ATP (JSON Schema primary)
JSON Schema Specifications
Schema structure ($schema, $id, type, properties)
Required vs optional fields
Data types and formats
Validation rules (min, max, pattern, enum)
References and composition ($ref, allOf, anyOf)
Schema Registry
Centralized schema storage
Schema versioning and history
Schema validation at publish/consume
Schema discovery and documentation
Schema Documentation
Description for each field
Examples and default values
Validation rules explanation
Consumer guidance
Schema Validation
Producer-side validation (before publish)
Consumer-side validation (after receive)
Runtime schema validation
Schema validation in CI/CD

Code Examples: - Complete JSON Schema examples for all event types - Schema validation code (C#) - Schema registry integration - Protocol Buffers definitions (optional)

Diagrams: - Schema structure - Schema registry architecture - Validation flow

Deliverables: - JSON Schema library for all events - Schema registry setup - Validation framework - Schema documentation

Topic 8: Event Versioning & Evolution¶

What will be covered:

Why Event Versioning Matters
Schema changes over time
Multiple versions in production simultaneously
Consumer compatibility
Zero-downtime deployments
Versioning Strategies
Explicit Versioning: Version in event name (AuditEventReceivedV2)
Schema Versioning: Version field in event metadata
Content Negotiation: Consumer specifies accepted versions
Compatibility Rules
Backward Compatibility: New consumers read old events
Forward Compatibility: Old consumers read new events
Full Compatibility: Both directions work
Breaking Changes: Incompatible, requires migration
Schema Evolution Patterns
Adding Optional Fields: Backward compatible
Removing Fields: Forward compatible (if optional)
Renaming Fields: Breaking change (use both temporarily)
Changing Types: Breaking change
Adding Required Fields: Breaking change
Migration Strategies
Expand-Contract: Gradual migration over time
Dual Publishing: Publish both versions temporarily
Event Transformation: Transform old to new format
Versioned Consumers: Multiple consumer versions
ATP Versioning Approach
Explicit version in event name for breaking changes
Schema version in metadata for tracking
Prefer additive changes (optional fields)
Document all changes in changelog

Code Examples: - Versioned event classes - Schema migration scripts - Dual publishing code - Event transformation code

Diagrams: - Compatibility matrix - Expand-contract pattern - Migration timeline - Versioning decision tree

Deliverables: - Versioning strategy guide - Compatibility rules - Migration procedures - Changelog templates

CYCLE 5: Event Publishing & Handling (~3,000 lines)¶

Topic 9: Event Publishing Patterns¶

What will be covered:

Transactional Outbox Pattern
Problem: Publishing events atomically with DB changes
Solution: Write event to outbox table in same transaction
Outbox processor: Background job publishes events
Guarantees: Exactly-once semantics (eventually)
Outbox Implementation in ATP
Outbox table schema (EventId, Payload, Published, Attempts)
Entity Framework Core integration
Outbox processor design
Idempotency guarantees
Performance optimization (batching)
Direct Publishing
Publish immediately after aggregate commit
Risk: Event published but DB rollback
Use case: Non-critical events
Event Batching
Batch multiple events for efficiency
Batch size and timing considerations
Partial failure handling
Publisher Retry Logic
Retry on transient failures
Exponential backoff
Max retry limit
DLQ for persistent failures
Publisher Monitoring
Publish success/failure metrics
Latency tracking
Outbox backlog monitoring
Alerting on anomalies

Code Examples: - Outbox table EF Core entity - Outbox pattern implementation - Publisher service code - Retry logic - Monitoring integration

Diagrams: - Transactional outbox flow - Outbox processor architecture - Retry mechanism - Monitoring dashboard

Deliverables: - Outbox pattern implementation - Publisher service code - Retry configuration - Monitoring setup

Topic 10: Event Handling Patterns¶

What will be covered:

Idempotent Event Handlers
Why idempotency is critical (at-least-once delivery)
Idempotency key strategies (EventId, CorrelationId)
Idempotency storage (cache, database)
Idempotency expiration
Idempotency Implementation
Check if event already processed
Store result for duplicate detection
Return cached result for duplicates
TTL for idempotency records
Event Deduplication
Deduplication at consumer side
Message fingerprinting
Deduplication window
Azure Service Bus duplicate detection
Event Ordering
Session-based ordering in Service Bus
Partition key for ordering
Out-of-order event handling
Sequence numbers
Handler Retry Logic
Retry on transient failures (network, timeouts)
Don't retry on permanent failures (validation)
Exponential backoff
Max retry attempts
Dead-Letter Queue (DLQ) Processing
Events moved to DLQ after max retries
DLQ monitoring and alerting
Manual review and reprocessing
Poison message handling
Compensating Actions
Undo operations when needed
Saga pattern for distributed transactions
Compensation event publishing

Code Examples: - Idempotent handler base class - Idempotency checker implementation - Deduplication logic - Retry policy configuration - DLQ processor - Compensating action example

Diagrams: - Idempotency flow - Deduplication mechanism - Retry and DLQ flow - Saga compensation

Deliverables: - Idempotency framework - Handler templates - DLQ procedures - Compensation patterns

CYCLE 6: Event Sourcing & CQRS (~3,000 lines)¶

Topic 11: Event Sourcing Patterns¶

What will be covered:

Event Sourcing Fundamentals
Events as source of truth
State reconstruction from events
Append-only event store
Benefits: Complete history, audit trail, time travel
Challenges: Query complexity, eventual consistency
Event Store Design
Event store schema (Streams, Events, Snapshots)
Event partitioning (by TenantId, StreamId)
Event ordering and versioning
Storage backend (Azure SQL, Cosmos DB, Event Store DB)
Event Streams
Stream per aggregate instance
Stream naming conventions
Stream lifecycle (active, sealed, archived)
Stream metadata (version, snapshot pointer)
Snapshots
Why snapshots (performance optimization)
Snapshot frequency (every N events)
Snapshot storage
State reconstruction: Snapshot + subsequent events
Event Replay
Full replay: Rebuild state from all events
Partial replay: From snapshot forward
Replay for debugging
Replay for projections
Event Store Operations
Append event to stream
Read events from stream
Read events from all streams (global log)
Snapshot creation
Stream archival

Code Examples: - Event store interface - Event stream implementation - Snapshot creation and loading - Event replay logic - EF Core event store mapping

Diagrams: - Event sourcing architecture - Event store schema - Snapshot and replay - Stream lifecycle

Deliverables: - Event store implementation - Snapshot strategy - Replay procedures - Performance optimization

Topic 12: CQRS and Projections¶

What will be covered:

CQRS Overview
Command Query Responsibility Segregation
Write model (commands, aggregates, events)
Read model (queries, projections, denormalized)
Benefits: Scalability, optimized models
ATP CQRS architecture
Command Handling
Command validation
Aggregate loading (from event store)
Business logic execution
Event generation
Event publishing
Projections
Event handlers that build read models
Denormalized views for queries
Multiple projections from same events
Projection examples in ATP
ATP Projections
AuditEventProjection: Queryable audit events
EventStreamProjection: Stream summaries
TenantProjection: Tenant overview
ClassificationProjection: Classification statistics
RetentionProjection: Retention timelines
Projection Implementation
Subscribe to events
Update read model on event
Handle projection failures
Projection versioning
Projection Rebuilding
Rebuild from event store (replay all events)
Incremental rebuild (from last checkpoint)
Blue-green projection deployment
Eventual Consistency
Write model and read model lag
Consistency guarantees
User experience considerations
Consistency monitoring

Code Examples: - Command handler implementation - Projection handler code - Read model entities - Projection rebuild scripts - Consistency monitoring

Diagrams: - CQRS architecture - Command and event flow - Projection updates - Eventual consistency

Deliverables: - CQRS implementation guide - Projection specifications - Rebuild procedures - Consistency monitoring

CYCLE 7: Event Processing & Projections (~2,500 lines)¶

Topic 13: Event Processing Pipelines¶

What will be covered:

Event Processing Stages
Ingestion → Validation → Enrichment → Transformation → Storage
Pipeline orchestration
Stage failures and retries
Event Enrichment
Adding contextual data to events
Tenant lookup
User profile enrichment
Geo-location enrichment
Event Transformation
Format conversion
Schema migration
Data normalization
PII redaction
Stream Processing
Real-time event processing
Windowing and aggregation
Complex event processing (CEP)
Azure Stream Analytics integration
Batch Processing
Scheduled batch jobs
Event aggregation
Analytics and reporting
Cold storage archival

Code Examples: - Processing pipeline implementation - Enrichment services - Transformation logic - Stream Analytics queries

Diagrams: - Processing pipeline - Enrichment flow - Stream processing architecture

Deliverables: - Pipeline implementation - Enrichment services - Stream Analytics queries

Topic 14: Projection Management¶

What will be covered:

Projection Lifecycle
Creation, updates, rebuilding, archival
Versioning projections
Blue-green deployments
Projection Checkpointing
Track last processed event
Resume from checkpoint
Checkpoint storage
Projection Scaling
Partitioned projections
Parallel processing
Load balancing
Projection Monitoring
Lag monitoring
Throughput metrics
Error tracking
Projection Testing
Unit testing projections
Integration testing with events
Replay testing

Code Examples: - Projection checkpoint implementation - Partitioned projection code - Monitoring integration - Test examples

Diagrams: - Projection lifecycle - Checkpointing mechanism - Scaling architecture

Deliverables: - Projection management guide - Checkpoint implementation - Monitoring setup - Testing framework

CYCLE 8: Event Reliability & Patterns (~2,500 lines)¶

Topic 15: Reliability Patterns¶

What will be covered:

At-Least-Once Delivery
Guaranteed delivery semantics
Duplicate handling
Idempotency requirements
Exactly-Once Semantics
Transactional outbox
Idempotent processing
Deduplication
Circuit Breaker Pattern
Protect downstream services
Failure detection
Automatic recovery
Bulkhead Pattern
Isolate failures
Resource partitioning
Failure containment
Timeout and Retry
Timeout configuration
Retry strategies
Backoff policies

Code Examples: - Circuit breaker implementation - Bulkhead pattern - Retry with Polly library

Diagrams: - Circuit breaker states - Bulkhead isolation - Retry flow

Deliverables: - Reliability patterns guide - Polly policy configurations - Failure handling procedures

Topic 16: Advanced Event Patterns¶

What will be covered:

Saga Pattern
Distributed transactions via events
Saga orchestration
Compensation logic
Event Sourcing + CQRS
Combined pattern
Benefits and trade-offs
Event Versioning Patterns
Upcasting (old → new)
Downcasting (new → old)
Event transformation
Snapshot Strategies
Frequency and triggers
Snapshot storage
Snapshot optimization

Code Examples: - Saga coordinator - Event upcasting - Snapshot creation

Diagrams: - Saga flow - Event transformation - Snapshot strategy

Deliverables: - Saga implementation - Versioning framework - Snapshot optimization

CYCLE 9: Event Observability & Compliance (~2,500 lines)¶

Topic 17: Event Observability¶

What will be covered:

Distributed Tracing
OpenTelemetry integration
Trace context propagation (W3C Trace Context)
Correlation IDs across events
Causation IDs (event chains)
Azure Application Insights integration
Event Metrics
Publish rate, consume rate
Processing latency
Error rates
Queue depth
Event Logging
Structured logging
Event lifecycle logging
Error and exception logging
Event Monitoring Dashboards
Grafana dashboards
Azure Monitor workbooks
Real-time monitoring
Event Alerting
Alert rules
Alert routing
On-call integration

Code Examples: - OpenTelemetry instrumentation - Metrics collection - Logging configuration - Dashboard JSON

Diagrams: - Distributed tracing - Monitoring architecture - Alert flow

Deliverables: - Observability framework - Dashboards - Alert configurations

Topic 18: Event Compliance & Audit¶

What will be covered:

Event Audit Trails
Complete event history
Immutable event log
Audit log retention
Event Retention Policies
Per-event-type retention
Compliance requirements (GDPR, HIPAA)
Archival to cold storage
Event Encryption
Payload encryption
Key management
Field-level encryption
Event Access Control
RBAC for events
Tenant isolation
Audit of access
GDPR Compliance
Right to be forgotten (event deletion)
Data portability (event export)
Audit trail requirements

Code Examples: - Audit logging - Retention policy implementation - Encryption/decryption - GDPR deletion

Diagrams: - Audit trail architecture - Retention lifecycle - Encryption flow

Deliverables: - Compliance guide - Retention policies - Encryption framework - GDPR procedures

CYCLE 10: Event Testing & Documentation (~2,500 lines)¶

Topic 19: Event Testing Strategies¶

What will be covered:

Contract Testing
Schema validation tests
Producer contract tests
Consumer contract tests
Pact framework integration
Event Replay Testing
Replay production events in test
Test projections with replay
Time-travel debugging
Chaos Engineering
Fault injection
Message loss simulation
Delayed messages
Duplicate messages
Load Testing
High-volume event generation
Throughput testing
Latency testing
Backpressure testing
Integration Testing
End-to-end event flows
Cross-service testing
Test event fixtures

Code Examples: - Contract test examples - Replay test harness - Chaos testing setup - Load testing scripts

Diagrams: - Testing strategy - Chaos scenarios - Load test architecture

Deliverables: - Test frameworks - Contract tests - Chaos procedures - Load testing suite

Topic 20: Event Documentation & Tooling¶

What will be covered:

Event Catalog Maintenance
Living documentation
Automated documentation generation
Schema documentation
Event Discovery Tools
Event browser UI
Schema registry UI
Event search and filtering
Event Debugging Tools
Event viewer
Event replay tools
Event trace analysis
Event Visualization
Event flow diagrams
Sequence diagrams
Event graphs
Developer Experience
Event SDKs
Code generators from schemas
Event templates
Documentation portals

Code Examples: - Documentation generators - Event browser implementation - Replay tools - SDK usage

Diagrams: - Tooling architecture - Event browser UI - Visualization examples

Deliverables: - Documentation automation - Event tooling - SDK and examples - Developer portal

Summary & Implementation Plan¶

Implementation Phases¶

Phase 1: Foundations (Cycles 1-2) - 2 months - EDA fundamentals and event types - Domain events catalog

Phase 2: Integration (Cycles 3-4) - 1.5 months - Integration events and routing - Schemas and versioning

Phase 3: Patterns (Cycles 5-6) - 2 months - Publishing and handling patterns - Event sourcing and CQRS

Phase 4: Operations (Cycles 7-8) - 1.5 months - Processing and projections - Reliability patterns

Phase 5: Production (Cycles 9-10) - 1.5 months - Observability and compliance - Testing and documentation

Success Metrics¶

Event Catalog: 100% of events documented
Schema Coverage: All events have JSON Schema
Contract Tests: All events have contract tests
Delivery Success: >99.9% event delivery
Processing Latency: <100ms P99
Idempotency: 100% idempotent handlers

Ownership & Maintenance¶

Backend Developers: Cycles 1-2, 5, 7
Architects: Cycles 1, 3, 6, 8
Integration Engineers: Cycles 3-4
QA Engineers: Cycles 9-10
Operations: Cycles 9

Document Status: ✅ Plan Approved - Ready for Content Generation

Target Start Date: Q2 2025

Expected Completion: Q3 2025

Owner: Backend Engineering Team

Last Updated: 2024-10-30