Event Sourcing - Learning Module

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When to Use Event Sourcing

The Decision Framework

You've learned what event sourcing is, how it works, and the tradeoffs involved. Now comes the practical question: Should you use event sourcing for your project?

This isn't a question with a universal answer. Event sourcing is a powerful pattern that introduces real complexity. The goal is not to use it everywhere, but to use it where it provides outsized value relative to its costs.

This page provides:

Decision criteria: Clear factors to evaluate for your specific context
Domain fit analysis: Recognizing domains where event sourcing shines
Real-world examples: Industry cases showing when event sourcing was (and wasn't) the right choice
Adoption strategies: Practical approaches for introducing event sourcing safely

What You Will Learn

By the end of this page, you will have a concrete decision framework for evaluating event sourcing, understand domain characteristics that indicate good fit, see real-world examples of event sourcing in production, and know practical adoption strategies for introducing event sourcing to your organization.

The Core Decision Criteria

Evaluate these criteria for your specific project. Not all criteria need to favor event sourcing—but the more that do, the stronger the case.

Tier 1: Strong Indicators (High Weight)

These factors, if present, make a compelling case for event sourcing.

Strong Indicators for Event Sourcing
Criterion	Pro-ES Signal	Anti-ES Signal
Audit Requirements	Regulatory mandate for complete history	No audit requirements
Temporal Queries	Core business need for point-in-time state	Only current state matters
Domain Model	Naturally event-driven; processes as flows	Simple CRUD entities
Team Experience	Prior event sourcing experience	First ES project under deadline
Consistency Tolerance	Eventual consistency acceptable	Immediate consistency required everywhere

Tier 2: Supporting Factors (Medium Weight)

These strengthen the case but aren't decisive on their own.

Supporting Factors
Criterion	Pro-ES Signal	Anti-ES Signal
Integration Complexity	Many downstream consumers	Single monolith, few integrations
Debugging Difficulty	Production issues are hard to reproduce	Issues are straightforward to diagnose
Schema Stability	Domain model is well-understood	Rapid prototyping, unclear requirements
Scale Expectations	High write volume with read scaling	Low scale, simple CRUD sufficient
Future Flexibility	Unknown future query patterns likely	Requirements are fixed and known

decision-evaluation.ts
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/**
 * Event Sourcing Decision Evaluator
 * A structured approach to assessing fit
 */
 
interface CriterionScore {
  criterion: string;
  weight: 'high' | 'medium' | 'low';
  score: -2 | -1 | 0 | 1 | 2; // Strong anti, weak anti, neutral, weak pro, strong pro
  notes: string;
}
 
interface DecisionResult {
  recommendation: 'yes' | 'no' | 'consider';
  totalScore: number;
  maxPossibleScore: number;
  percentageScore: number;
  criticalBlockers: string[];
  strongIndicators: string[];
}
 
function evaluateEventSourcingFit(scores: CriterionScore[]): DecisionResult {
  const weights = { high: 3, medium: 2, low: 1 };
  
  let totalScore = 0;
  let maxScore = 0;
  const criticalBlockers: string[] = [];
  const strongIndicators: string[] = [];
  
  for (const item of scores) {
    const weight = weights[item.weight];
    totalScore += item.score * weight;
    maxScore += 2 * weight; // Max possible is +2 per criterion
    
    // Identify critical blockers (high weight + strongly negative)
    if (item.weight === 'high' && item.score <= -2) {
      criticalBlockers.push(`${item.criterion}: ${item.notes}`);
    }
    
    // Identify strong indicators (high weight + strongly positive)
    if (item.weight === 'high' && item.score >= 2) {
      strongIndicators.push(`${item.criterion}: ${item.notes}`);
    }
  }
  
  const percentage = ((totalScore + maxScore) / (2 * maxScore)) * 100;
  
  // Decision logic
  let recommendation: 'yes' | 'no' | 'consider';
  
  if (criticalBlockers.length > 0) {
    recommendation = 'no';
  } else if (strongIndicators.length >= 2 && percentage > 65) {
    recommendation = 'yes';
  } else if (percentage > 50) {
    recommendation = 'consider';
  } else {
    recommendation = 'no';
  }
  
  return {
    recommendation,
    totalScore,
    maxPossibleScore: maxScore,
    percentageScore: percentage,
    criticalBlockers,
    strongIndicators,
  };
}
 
// Example evaluation for a financial services project
const financialServicesEvaluation: CriterionScore[] = [
  {
    criterion: 'Audit Requirements',
    weight: 'high',
    score: 2,
    notes: 'SOX compliance mandates complete transaction history',
  },
  {
    criterion: 'Temporal Queries',
    weight: 'high',
    score: 2,
    notes: 'Regulators require point-in-time account state for audits',
  },
  {
    criterion: 'Domain Model',
    weight: 'high',
    score: 2,
    notes: 'Trading is naturally event-driven (orders, fills, settlements)',
  },
  {
    criterion: 'Team Experience',
    weight: 'high',
    score: 1,
    notes: 'Some team members have prior ES experience',
  },
  {
    criterion: 'Consistency Tolerance',
    weight: 'high',
    score: 0,
    notes: 'Needs strong consistency for balance checks',
  },
  {
    criterion: 'Integration Complexity',
    weight: 'medium',
    score: 2,
    notes: 'Multiple downstream systems (risk, reporting, notifications)',
  },
];
 
const result = evaluateEventSourcingFit(financialServicesEvaluation);
// Result: { recommendation: 'yes', strongIndicators: [...], ... }

Watch for Blockers

A single critical blocker (like team inexperience under tight deadline, or hard requirement for immediate consistency everywhere) can override many positive signals. Be honest about blockers—they won't disappear because of enthusiasm for the pattern.

Domain Fit Analysis

Some domains are naturally event-oriented—the business processes they model are inherently sequences of things happening. In these domains, event sourcing aligns with how domain experts already think.

Signals That Your Domain Is Event-Oriented:

Domain experts speak in events: "When a customer places an order..." "After the payment is confirmed..."
Processes have clear lifecycle stages: Proposals → Negotiations → Contracts → Amendments → Closures
History matters more than current state: Medical records, financial transactions, legal proceedings
Time is fundamental: The sequence and timing of events affects outcomes
Audit requirements exist: Proving what happened, when, and why

Domain Fit Examples
Domain	Event-Oriented?	Key Events	Why ES Fits (or Doesn't)
Financial Trading	Yes	OrderPlaced, Filled, Settled, Cancelled	Trades are events; regulatory audit required; temporal queries mandatory
Healthcare Records	Yes	DiagnosisRecorded, PrescriptionIssued, TreatmentStarted	Medical history is sacred; changes must be explained; point-in-time state critical
E-commerce Orders	Moderate	OrderPlaced, Shipped, Delivered, Returned	Order lifecycle is event-driven but product catalog is CRUD
Content Management	Low	ArticlePublished, ArticleEdited	Content is mutable state; history less important than current version
User Profiles	Low	ProfileUpdated, SettingsChanged	Simple CRUD; history rarely queried; ES is overkill
IoT Sensor Data	Yes	ReadingCaptured, ThresholdExceeded, CalibrationPerformed	Time-series data is inherently event-based; append-only fits perfectly

The Bounded Context Question

Event sourcing doesn't have to be all-or-nothing. In a larger system, some bounded contexts may benefit greatly while others don't. Consider:

Order Management: Event-sourced (complex lifecycle, audit needs)
Product Catalog: Traditional CRUD (mutable, rarely audited)
Customer Profiles: CQRS without event sourcing (read-heavy, write-simple)
Payment Processing: Event-sourced (regulatory requirements, integration-heavy)

Choose the right persistence strategy for each bounded context based on its specific requirements.

bounded-context-analysis.ts
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/**
 * Per-bounded-context persistence strategy analysis
 */
 
interface BoundedContextAnalysis {
  name: string;
  characteristics: {
    auditRequired: boolean;
    temporalQueriesNeeded: boolean;
    complexLifecycle: boolean;
    integrationHeavy: boolean;
    readWriteRatio: 'write-heavy' | 'balanced' | 'read-heavy';
    consistencyRequirement: 'strong' | 'eventual-ok';
  };
  recommendedStrategy: 'event-sourcing' | 'cqrs-no-es' | 'crud';
  reasoning: string;
}
 
const ecommerceSystemAnalysis: BoundedContextAnalysis[] = [
  {
    name: 'Order Management',
    characteristics: {
      auditRequired: true,
      temporalQueriesNeeded: true, // "What was the order status on day X?"
      complexLifecycle: true, // Draft → Placed → Paid → Shipped → Delivered
      integrationHeavy: true, // Inventory, Payments, Shipping, Notifications
      readWriteRatio: 'balanced',
      consistencyRequirement: 'eventual-ok',
    },
    recommendedStrategy: 'event-sourcing',
    reasoning: 'Complex lifecycle with audit requirements and heavy integration needs - perfect ES fit',
  },
  {
    name: 'Product Catalog',
    characteristics: {
      auditRequired: false,
      temporalQueriesNeeded: false,
      complexLifecycle: false, // Products are just updated
      integrationHeavy: false, // Read by other systems, rarely triggers events
      readWriteRatio: 'read-heavy', // Millions of reads per update
      consistencyRequirement: 'eventual-ok',
    },
    recommendedStrategy: 'crud',
    reasoning: 'Simple CRUD with heavy read caching - ES complexity not justified',
  },
  {
    name: 'Inventory Management',
    characteristics: {
      auditRequired: true, // Track who/when for inventory discrepancies
      temporalQueriesNeeded: true, // "What was stock level when order placed?"
      complexLifecycle: false, // Just quantity changes
      integrationHeavy: true, // Order service needs to check/reserve
      readWriteRatio: 'write-heavy', // Every order affects inventory
      consistencyRequirement: 'strong', // Must prevent overselling
    },
    recommendedStrategy: 'event-sourcing',
    reasoning: 'Audit needs plus integration needs, but requires strong consistency for reservations',
  },
  {
    name: 'Customer Accounts',
    characteristics: {
      auditRequired: false, // Basic profile changes don't need full audit
      temporalQueriesNeeded: false,
      complexLifecycle: false,
      integrationHeavy: false,
      readWriteRatio: 'read-heavy',
      consistencyRequirement: 'eventual-ok',
    },
    recommendedStrategy: 'cqrs-no-es',
    reasoning: 'Read-heavy with simple writes - CQRS for read scaling without ES complexity',
  },
  {
    name: 'Payment Processing',
    characteristics: {
      auditRequired: true, // Financial compliance
      temporalQueriesNeeded: true, // Dispute resolution
      complexLifecycle: true, // Authorized → Captured → Settled
      integrationHeavy: true, // Payment providers, fraud detection
      readWriteRatio: 'balanced',
      consistencyRequirement: 'strong', // Double-charge prevention
    },
    recommendedStrategy: 'event-sourcing',
    reasoning: 'Critical audit needs, complex lifecycle, strong consistency via aggregate boundaries',
  },
];
 
// System overview
function generateSystemOverview(contexts: BoundedContextAnalysis[]): void {
  console.log('=== Persistence Strategy Overview ===\n');
  
  const grouped = {
    'event-sourcing': contexts.filter(c => c.recommendedStrategy === 'event-sourcing'),
    'cqrs-no-es': contexts.filter(c => c.recommendedStrategy === 'cqrs-no-es'),
    'crud': contexts.filter(c => c.recommendedStrategy === 'crud'),
  };
  
  for (const [strategy, items] of Object.entries(grouped)) {
    console.log(`${strategy.toUpperCase()}:`);
    for (const item of items) {
      console.log(`  - ${item.name}: ${item.reasoning}`);
    }
    console.log();
  }
}

Real-World Case Studies

Let's examine real-world examples of event sourcing in production—both successful implementations and cases where it wasn't the right choice.

Success Stories

•LinkedIn (Kafka): LinkedIn created Kafka, a distributed event log that fundamentally enables event sourcing at massive scale. Their activity streams, newsfeed, and analytics are all powered by event processing. Key insight: events as first-class infrastructure, not just an application pattern.
•Netflix (Event Sourcing for Playback State): Netflix uses event sourcing to track user playback state—allowing resume on any device and enabling personalization through watch history analysis. The event stream of watches, pauses, and completions powers recommendations.
•LMAX Digital Asset Exchange: A financial exchange using event sourcing for trade processing. Every trade is an event; the order book is derived state. They achieve deterministic replay, complete audit trails, and the ability to replay entire trading days for analysis.
•Walmart (Event-Driven Inventory): Walmart's inventory management system uses event sourcing to track stock movements across thousands of stores. Events like 'ItemReceivedAtWarehouse' and 'ItemSoldAtStore' provide complete supply chain visibility.

When Teams Stepped Back

•A Startup's MVP: A team adopted event sourcing for a social media clone's MVP. With rapidly changing requirements and a tight deadline, they found ES added complexity without proportional benefit. They reverted to traditional CRUD and shipped 3x faster. Lesson: Know when simplicity wins.
•CRUD-Heavy Admin System: An internal admin tool for managing configurations adopted ES for 'future-proofing'. The domain was simple CRUD with no audit requirements. Projections added latency to simple updates. They migrated back to PostgreSQL. Lesson: Match pattern to domain.
•Team Without Experience: A team adopted ES for a greenfield project without anyone having prior ES experience. The learning curve, combined with production pressure, led to bugs in event handling and projection management. They hired an ES consultant and recovered, but lost months. Lesson: Invest in expertise.

The Common Theme

Successful event sourcing implementations share a pattern: the domain is genuinely event-oriented, the team has relevant experience (or time to learn), and the benefits (audit, temporal queries, integration) are genuine requirements—not theoretical future possibilities.

Adoption Strategies

If you've decided event sourcing is right for your context, how do you introduce it safely? Here are proven adoption strategies.

Adoption Approaches
Approach	Description	When to Use	Risk Level
Greenfield Context	Apply ES to a new bounded context with no legacy	New business capability being built	Low
Parallel Running	Run ES system alongside existing; compare results	High-stakes migration needing validation	Medium
Strangler Pattern	Gradually migrate functionality from legacy to ES	Large existing system being modernized	Medium
Event Enablement First	Add event publishing to existing system before full ES	Want integration benefits now, full ES later	Low
Big Bang Migration	Full cutover to ES system at once	Small system, greenfield-like simplicity	High

adoption-strategies.ts
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/**
 * Illustration of the Strangler Pattern for ES adoption
 */
 
// Phase 1: Existing CRUD system continues; add event publishing
class Phase1LegacyOrderService {
  constructor(
    private database: LegacyDatabase,
    private eventBus: EventBus // NEW: Add event publishing
  ) {}
 
  async placeOrder(order: OrderData): Promise<Order> {
    // Existing CRUD logic unchanged
    const newOrder = await this.database.insert('orders', order);
    
    // NEW: Publish event for downstream systems
    await this.eventBus.publish('OrderPlaced', {
      orderId: newOrder.id,
      customerId: order.customerId,
      items: order.items,
      timestamp: new Date(),
    });
    
    return newOrder;
  }
}
 
// Phase 2: New consumers use events; legacy reads continue
class Phase2OrderQueryService {
  constructor(
    private legacyDb: LegacyDatabase, // Still used for reads
    private eventProjection: EventProjectionService // NEW: Can use projection too
  ) {}
 
  async getOrder(orderId: string): Promise<Order> {
    // Try new projection first (eventually consistent)
    const projected = await this.eventProjection.getOrder(orderId);
    if (projected) return projected;
    
    // Fall back to legacy DB
    return this.legacyDb.query('orders', orderId);
  }
}
 
// Phase 3: New writes go to event store; legacy reads phased out
class Phase3EventSourcedOrderService {
  constructor(
    private eventStore: EventStore,
    private legacyDb: LegacyDatabase // Still needed during transition
  ) {}
 
  async placeOrder(command: PlaceOrderCommand): Promise<Order> {
    // NEW: Write to event store
    const order = new OrderAggregate(command.orderId);
    order.placeOrder(command.customerId, command.items);
    
    await this.eventStore.append(
      `order-${command.orderId}`,
      'no_stream',
      order.getUncommittedEvents()
    );
    
    // TRANSITIONAL: Also write to legacy DB for backward compatibility
    await this.legacyDb.insert('orders', this.toLegacyFormat(order));
    
    return order.toOrder();
  }
}
 
// Phase 4: Full event sourcing; legacy DB retired
class Phase4FullEventSourcing {
  constructor(
    private eventStore: EventStore,
    private orderProjection: OrderProjection
  ) {}
 
  // Commands go to event store via aggregates
  async placeOrder(command: PlaceOrderCommand): Promise<Order> {
    const order = new OrderAggregate(command.orderId);
    order.placeOrder(command.customerId, command.items);
    
    await this.eventStore.append(
      `order-${command.orderId}`,
      'no_stream',
      order.getUncommittedEvents()
    );
    
    return order.toOrder();
  }
 
  // Queries go to projections (or aggregate for consistency)
  async getOrder(orderId: string): Promise<Order | null> {
    return this.orderProjection.getOrder(orderId);
  }
 
  async getOrderWithConsistency(orderId: string): Promise<Order | null> {
    // When strong consistency needed, reconstitute aggregate
    const events = await this.eventStore.readStream(`order-${orderId}`);
    if (events.events.length === 0) return null;
    
    const order = new OrderAggregate(orderId);
    order.loadFromHistory(events.events);
    return order.toOrder();
  }
}
 
/**
 * Migration timeline example
 */
const migrationPlan = {
  phase1: {
    duration: '4-6 weeks',
    goals: [
      'Add event publishing to existing system',
      'Set up event infrastructure (Kafka/EventStore)',
      'Validate events are flowing correctly',
    ],
    rollbackPlan: 'Remove event publishing code; minimal risk',
  },
  phase2: {
    duration: '6-8 weeks',
    goals: [
      'Build projections consuming events',
      'Route some reads to projections',
      'Monitor for consistency issues',
    ],
    rollbackPlan: 'Route all reads back to legacy DB',
  },
  phase3: {
    duration: '8-12 weeks',
    goals: [
      'New writes go to event store',
      'Dual-write to legacy for compatibility',
      'Migrate remaining reads to projections',
    ],
    rollbackPlan: 'Switch writes back to legacy; events become secondary',
  },
  phase4: {
    duration: '4-6 weeks',
    goals: [
      'Stop dual-writes to legacy',
      'Archive legacy data',
      'Full event sourcing operational',
    ],
    rollbackPlan: 'Replay events to rebuild legacy DB if needed',
  },
};

Start With One Bounded Context

Don't try to event-source your entire system at once. Pick one bounded context—preferably one with strong audit or temporal requirements—and fully implement ES there. Learn from that experience before expanding. Success in one context builds team capability and organizational confidence.

Team Readiness Assessment

Event sourcing introduces concepts that may be unfamiliar to teams experienced only with traditional CRUD. Assess your team's readiness and plan for the learning curve.

Key Competencies Needed:

Technical Competencies

•Domain-Driven Design fundamentals: Aggregates, bounded contexts, domain events. ES is most effective when combined with DDD.
•Eventual consistency handling: Understanding, designing for, and debugging eventually consistent systems.
•Distributed systems basics: CAP theorem, consensus, idempotency, retry patterns.
•Schema evolution practices: Versioning, upcasting, defensive serialization.
•Operational tooling: Monitoring projections, replaying events, diagnosing lag.

Team Readiness Checklist
Area	Green Light	Yellow Light	Red Light
ES Experience	Team has production ES experience	Some members have ES exposure	No one has used ES before
DDD Knowledge	Team practices DDD actively	Familiar with concepts	New to DDD
Distributed Systems	Team operates distributed systems	Some exposure, learning	New to distributed systems
Time for Learning	Dedicated ramp-up period planned	Some time for learning	Tight deadline, no learning time
Expert Access	ES expert on team or available	Can hire consultant	No access to expertise

Learning Investment

If your team is new to event sourcing, plan for a learning investment:

Training: 2-4 weeks of study, workshops, katas before production work
Pair Programming: Have experienced members (or consultants) pair with less experienced
Proof of Concept: Build a non-critical system first to validate understanding
Post-Mortems: Plan for mistakes; conduct blameless reviews to accelerate learning

The investment pays off. Teams that rush into ES without preparation often spend more time debugging fundamental mistakes than they would have spent learning properly.

The Expertise Tax

Underestimating the expertise needed is the most common mistake. Event sourcing looks simple in tutorials but has subtle complexities in production—projection ordering issues, event schema evolution conflicts, debugging production state derivation. Plan for the learning curve, or pay the debugging tax later.

Infrastructure and Tooling Considerations

Event sourcing requires infrastructure that may be new to your organization. Evaluate your platform readiness.

Infrastructure Requirements

•Event Store: EventStoreDB, Kafka, PostgreSQL adapter, or cloud alternatives. Consider operational complexity, durability guarantees, and subscription model.
•Projection Infrastructure: Database(s) for read models, caching layers, potential search indexes. May be different from write-side store.
•Monitoring and Alerting: Projection lag tracking, event throughput metrics, dead-letter monitoring. Standard APM may not cover ES-specific concerns.
•Development Tooling: Event replay tools, projection rebuild scripts, local development event store. Developer experience matters for velocity.
•Backup and Recovery: Event store backup strategies, point-in-time recovery, cross-region replication for disaster recovery.

Event Store Technology Comparison for Different Needs
Need	EventStoreDB	Kafka	PostgreSQL	DynamoDB Streams
ES-Native Design	Yes	Partial	Adapter	Partial
Operational Familiarity	Lower	Medium	High	Medium (AWS)
Scale (events/sec)	10K+	Millions	10K-100K	Millions
Managed Offering	Cloud	Confluent	Any cloud	AWS Native
Projection Support	Built-in	External	External	Lambda-based
Global Ordering	Yes	Per-partition	Yes	Per-shard

Operational Considerations

On-Call Implications: Event store issues require different debugging skills. Prepare runbooks for common scenarios (projection lag, event replay, schema issues).
Capacity Planning: Event stores grow indefinitely. Plan storage, archival, and tiering strategies early.
Security: Events may contain sensitive data. Plan for encryption, access control, and compliance (GDPR right to erasure with crypto-shredding).
Multi-Region: If you need multi-region deployment, evaluate how event ordering and consistency work across regions.

Start Simple, Scale Later

For many teams, starting with PostgreSQL as an event store (with proper schema) is pragmatic. It leverages existing expertise and infrastructure. You can migrate to a specialized event store like EventStoreDB later if needed. Don't over-engineer infrastructure before validating the approach.

The Final Checklist

Before committing to event sourcing, work through this final checklist with your team and stakeholders.

Pre-Commitment Checklist

•Domain Fit Validated: We've analyzed our domain and confirmed it's genuinely event-oriented, not just CRUD with event logging bolted on.
•Requirements Justified: We have concrete requirements (audit, temporal queries, integration) that event sourcing satisfies—not just theoretical future possibilities.
•Team Prepared: We have ES experience on the team, or a concrete plan (training, consultants, time buffer) to acquire it.
•Consistency Understood: We've mapped which operations need strong consistency, which can be eventual, and have mitigation strategies.
•Infrastructure Planned: We've chosen an event store technology and understand its operational requirements.
•Schema Strategy Defined: We have a plan for event versioning and schema evolution from day one.
•Adoption Approach Chosen: We're starting with a defined scope (one bounded context, greenfield) rather than a big-bang migration.
•Success Metrics Defined: We know how we'll measure if event sourcing is delivering value.

Go Ahead If...

• 7+ checklist items are confidently checked • No blockers identified in high-weight criteria • Team has relevant experience or learning time • Domain is genuinely event-oriented • Stakeholders understand the tradeoffs

Reconsider If...

• Multiple checklist items are unchecked • Critical blockers present (deadline, consistency, inexperience) • Domain is primarily CRUD • Event sourcing is chosen for 'future-proofing' without concrete present needs

Summary: When to Use Event Sourcing

We've provided a complete framework for deciding when event sourcing is the right choice. Let's consolidate the key decision factors:

Key Takeaways

•Evaluate systematically — Use structured criteria, not gut feeling or hype, to assess fit
•Domain fit is primary — Event-oriented domains benefit most; CRUD domains often don't justify complexity
•Requirements must be concrete — Audit, temporal queries, and integration should be real needs, not theoretical
•Team experience matters critically — Lack of experience under pressure is a valid blocker
•Start small, validate, expand — One bounded context at a time, with lessons learned informing expansion
•Infrastructure and tooling investment — Plan for the platform changes ES requires
•Adoption approach reduces risk — Strangler pattern and parallel running are safer than big-bang

Module Complete

You've now completed the Event Sourcing module. You've learned:

What event sourcing is — Storing events as the source of truth, deriving state through replay
How to design event stores — Schema, streams, optimistic concurrency, subscriptions
How to rebuild state — Aggregates, projections, left-fold pattern, consistency boundaries
The real tradeoffs — Audit benefits vs. eventual consistency challenges
When to use it — Decision framework, domain fit, adoption strategies

Event sourcing is a powerful pattern for the right context. Use this knowledge to make informed decisions—adopting event sourcing where it shines, and avoiding it where simpler approaches suffice.

Module Complete

Congratulations! You've completed Event Sourcing. You now have the knowledge to evaluate, design, and implement event-sourced systems—and equally important, to know when not to. The next module in Chapter 16 (Saga Pattern) will build on these concepts, showing how to coordinate business processes across event-sourced aggregates.