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Event sourcing is not a silver bullet. Like every architectural pattern, it offers specific benefits in exchange for specific costs. The teams that succeed with event sourcing are those that enter with clear eyes—understanding both the superpowers it grants and the operational burdens it imposes.
This page provides an honest assessment of event sourcing in production. We'll examine:
The goal is not to sell you on event sourcing—it's to equip you with the knowledge to make an informed architectural decision.
By the end of this page, you will understand the concrete benefits event sourcing provides (and when they matter most), the genuine challenges teams face (and when they're dealbreakers), mitigation strategies that make event sourcing practical, and decision criteria for whether event sourcing fits your context.
In event-sourced systems, the audit trail is not a feature—it's the architecture itself. Every state change is an immutable event with a timestamp, potentially including who initiated the action and why.
What This Enables
| Audit Need | CRUD Approach | Event Sourcing |
|---|---|---|
| Who changed this field? | Requires separate audit table; easily bypassed | Intrinsic—event metadata includes user |
| What was the value on date X? | Only with bi-temporal schema; rarely implemented | Replay events to date X |
| All changes to entity Y | Query audit table; hope it's complete | Read stream for entity Y |
| Prove compliance to auditors | Export audit logs; reconcile inconsistencies | Export events; mathematically consistent |
| Detect unauthorized changes | Compare current state to expected; hope nothing between | All changes are visible; nothing hidden |
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// Every event naturally carries audit informationinterface AuditableEvent { // WHAT happened eventType: string; aggregateId: string; payload: unknown; // WHEN it happened timestamp: Date; // WHO initiated it (from command context) metadata: { userId?: string; // Authenticated user impersonatingUser?: string; // Admin acting as user ipAddress?: string; // Request origin userAgent?: string; // Client information // WHY (business context) correlationId: string; // Business transaction ID causationId: string; // What triggered this event commandId?: string; // Original command // Additional context requestId?: string; // Request tracing sessionId?: string; // User session };} // Audit query examplesclass AuditQueryService { constructor(private eventStore: EventStore) {} /** * "Show me everything user X did on date Y" */ async getUserActivityOnDate( userId: string, date: Date ): Promise<AuditableEvent[]> { const startOfDay = new Date(date); startOfDay.setHours(0, 0, 0, 0); const endOfDay = new Date(date); endOfDay.setHours(23, 59, 59, 999); const allEvents = await this.eventStore.readAll({ fromTimestamp: startOfDay, toTimestamp: endOfDay, }); return allEvents.events.filter( e => e.metadata?.userId === userId ) as AuditableEvent[]; } /** * "What happened to order #123?" */ async getEntityHistory(entityType: string, entityId: string): Promise<{ events: AuditableEvent[]; timeline: TimelineEntry[]; }> { const stream = `${entityType}-${entityId}`; const result = await this.eventStore.readStream(stream); return { events: result.events as AuditableEvent[], timeline: result.events.map(e => ({ timestamp: e.timestamp, action: e.eventType, actor: e.metadata?.userId ?? 'system', summary: this.summarizeEvent(e), })), }; } /** * "Prove that balance was X on audit date Y" */ async getStateAsOfDate<T>( aggregateId: string, date: Date, reducer: (events: StoredEvent[]) => T ): Promise<{ state: T; provenBy: StoredEvent[]; lastEventBeforeDate: StoredEvent; }> { const result = await this.eventStore.readStream(aggregateId); const eventsBeforeDate = result.events.filter(e => e.timestamp <= date); return { state: reducer(eventsBeforeDate), provenBy: eventsBeforeDate, lastEventBeforeDate: eventsBeforeDate[eventsBeforeDate.length - 1], }; }}The most reliable audit system is one that cannot be bypassed—where the audit trail IS the data, not a log of the data. Event sourcing achieves this naturally. Contrast with CRUD systems where audit triggers can be disabled, audit tables can be modified, and application-level logging can be skipped.
When production incidents occur, event sourcing transforms debugging from guesswork into science. Instead of theorizing about what might have happened, you can replay the exact events and observe the system's behavior step by step.
The Debugging Workflow
This deterministic reproducibility is impossible in mutable-state systems where the path to current state is lost.
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/** * Debugging tools enabled by event sourcing */ // Tool 1: Event Stream Inspectorclass StreamInspector { constructor(private eventStore: EventStore) {} async inspect(streamId: string): Promise<void> { const result = await this.eventStore.readStream(streamId); console.log(`\n=== Stream: ${streamId} ===`); console.log(`Total events: ${result.events.length}`); console.log(`Current version: ${result.version}`); console.log(`\n--- Event Timeline ---`); for (const event of result.events) { console.log(`[${event.sequenceNumber}] ${event.timestamp.toISOString()}`); console.log(` Type: ${event.eventType}`); console.log(` Data: ${JSON.stringify(event.data, null, 2).slice(0, 200)}...`); console.log(); } }} // Tool 2: Step-by-step state replayclass StateReplayer { async replayWithInspection<TState>( events: StoredEvent[], initialState: TState, apply: (state: TState, event: StoredEvent) => TState, inspector?: (step: number, event: StoredEvent, before: TState, after: TState) => void ): Promise<TState[]> { const stateHistory: TState[] = [initialState]; let current = initialState; for (let i = 0; i < events.length; i++) { const event = events[i]; const before = current; const after = apply(current, event); stateHistory.push(after); if (inspector) { inspector(i, event, before, after); } current = after; } return stateHistory; }} // Example debugging sessionasync function debugOrderIssue(orderId: string): Promise<void> { const eventStore = new EventStore(); const inspector = new StreamInspector(eventStore); const replayer = new StateReplayer(); // 1. Get the event stream const stream = `order-${orderId}`; const result = await eventStore.readStream(stream); console.log(`Debugging order ${orderId}`); console.log(`Found ${result.events.length} events`); // 2. Replay with step-by-step inspection const stateHistory = await replayer.replayWithInspection( result.events, createEmptyOrderState(orderId), applyOrderEvent, (step, event, before, after) => { console.log(`\n--- Step ${step + 1} ---`); console.log(`Event: ${event.eventType} at ${event.timestamp}`); console.log(`Before: status=${before.status}, total=${before.total}`); console.log(`After: status=${after.status}, total=${after.total}`); // Check for anomalies if (after.total < 0) { console.error(`!!! ANOMALY: Negative total after ${event.eventType}`); } if (before.status === 'completed' && after.status !== 'completed') { console.error(`!!! ANOMALY: Status went backwards from completed`); } } ); // 3. Compare to expected end state const finalState = stateHistory[stateHistory.length - 1]; console.log(`\n=== Final State ===`); console.log(JSON.stringify(finalState, null, 2));} // Tool 3: Event diff between two streamsclass StreamDiffer { static diff( events1: StoredEvent[], events2: StoredEvent[] ): EventDiff { const diff: EventDiff = { onlyIn1: [], onlyIn2: [], different: [], }; const byId1 = new Map(events1.map(e => [e.eventId, e])); const byId2 = new Map(events2.map(e => [e.eventId, e])); for (const [id, event] of byId1) { if (!byId2.has(id)) { diff.onlyIn1.push(event); } else { const other = byId2.get(id)!; if (JSON.stringify(event.data) !== JSON.stringify(other.data)) { diff.different.push({ event1: event, event2: other }); } } } for (const [id, event] of byId2) { if (!byId1.has(id)) { diff.onlyIn2.push(event); } } return diff; }} // Tool 4: What-if analysisclass WhatIfAnalyzer { async analyzeWithoutEvent( streamId: string, eventIdToSkip: string ): Promise<{ with: unknown; without: unknown; difference: unknown }> { const result = await this.eventStore.readStream(streamId); const allEvents = result.events; const filteredEvents = allEvents.filter(e => e.eventId !== eventIdToSkip); const stateWith = this.aggregate.reconstitute(allEvents); const stateWithout = this.aggregate.reconstitute(filteredEvents); return { with: stateWith, without: stateWithout, difference: this.computeDiff(stateWith, stateWithout), }; }}A financial services team reported a customer's account showing the wrong balance. With event sourcing, they exported the account's 3,000 events, replayed locally, and found a bug in how a specific promotion was being applied—event #2,347 had incorrect discount calculation. They fixed the bug, verified with replay, and added a compensating event. Total time: 2 hours. In their previous CRUD system, similar issues took days of log analysis.
Event sourcing naturally enables event-driven integration. The event stream that powers your system can also power external systems, analytics pipelines, and future applications you haven't built yet.
Integration Advantages
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/** * Event-driven integration patterns */ // Pattern 1: Event Router to multiple consumersclass EventRouter { private handlers = new Map<string, EventHandler[]>(); registerHandler(eventType: string, handler: EventHandler): void { const existing = this.handlers.get(eventType) ?? []; this.handlers.set(eventType, [...existing, handler]); } async route(event: StoredEvent): Promise<void> { const handlers = this.handlers.get(event.eventType) ?? []; // Fan-out to all registered handlers await Promise.all( handlers.map(handler => handler(event).catch(err => { console.error(`Handler error for ${event.eventType}:`, err); // Dead-letter or retry logic here }) ) ); }} // Pattern 2: New consumer bootstrapping via replayclass NewConsumerBootstrap { constructor( private eventStore: EventStore, private checkpointStore: CheckpointStore ) {} async bootstrap( consumerId: string, handler: EventHandler, options: { startFrom?: Position; eventTypes?: string[]; progressCallback?: (position: number, total: number) => void; } = {} ): Promise<void> { const startPos = options.startFrom ?? 0; const headPos = await this.eventStore.getHeadPosition(); console.log(`Bootstrapping ${consumerId} from ${startPos} to ${headPos}`); let position = startPos; const batchSize = 1000; while (position <= headPos) { const result = await this.eventStore.readAll({ fromPosition: position, count: batchSize, eventTypes: options.eventTypes, }); if (result.events.length === 0) break; for (const event of result.events) { await handler(event); } position = result.events[result.events.length - 1].globalPosition + 1; // Save checkpoint periodically await this.checkpointStore.saveCheckpoint({ subscriptionId: consumerId, position, updatedAt: new Date(), }); if (options.progressCallback) { options.progressCallback(position, headPos); } } console.log(`Bootstrap complete for ${consumerId}`); }} // Pattern 3: Outbox-free publishing// In event sourcing, events ARE the outboxclass EventPublisher { constructor(private subscription: Subscription) {} async startPublishing( target: ExternalMessageBroker, transform: (event: StoredEvent) => ExternalMessage ): Promise<void> { // Events are already durable in the event store // No separate outbox table needed! await this.subscription.start({ onEvent: async (event) => { const message = transform(event); await target.publish(message); }, onError: async (error, event) => { // Retry logic or dead-letter console.error('Publish failed:', error, event); }, }); }} // Pattern 4: Analytics streamingclass AnalyticsExporter { constructor( private eventStore: EventStore, private dataWarehouse: DataWarehouse ) {} async exportForAnalytics(): Promise<void> { const lastExported = await this.dataWarehouse.getLastExportedPosition(); const result = await this.eventStore.readAll({ fromPosition: lastExported + 1, count: 10000, }); if (result.events.length === 0) return; // Transform events to analytics format const records = result.events.map(event => ({ event_id: event.eventId, event_type: event.eventType, aggregate_id: event.aggregateId, timestamp: event.timestamp, user_id: event.metadata?.userId, data: event.data, // Flatten as needed for your schema })); // Batch insert into data warehouse await this.dataWarehouse.batchInsert('events_raw', records); // Update checkpoint await this.dataWarehouse.setLastExportedPosition( result.events[result.events.length - 1].globalPosition ); }}Eventual consistency is not unique to event sourcing, but it's more visible. When projections update asynchronously, users may see stale data—an experience that can confuse or frustrate if not handled carefully.
The Core Problem
This "read-after-write inconsistency" is the most common complaint about eventually consistent systems.
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/** * Strategies for handling eventual consistency */ // Strategy 1: Read-your-writes guaranteeclass ConsistentReadService { constructor( private aggregateRepo: AggregateRepository, private readModelStore: ReadModelStore ) {} async getProfileWithConsistency( userId: string, expectedVersion?: number ): Promise<UserProfile> { const readModel = await this.readModelStore.get<UserProfile>( 'profiles', userId ); // If we have an expected version, wait or fallback if (expectedVersion !== undefined) { if (!readModel || readModel.version < expectedVersion) { // Option A: Wait briefly for projection const updated = await this.waitForProjection(userId, expectedVersion, 2000); if (updated) return updated; // Option B: Fall back to aggregate console.log(`Projection stale, falling back to aggregate for ${userId}`); const aggregate = await this.aggregateRepo.load(userId); return aggregate.toProfile(); } } return readModel!; } private async waitForProjection( userId: string, expectedVersion: number, timeoutMs: number ): Promise<UserProfile | null> { const startTime = Date.now(); const pollInterval = 100; while (Date.now() - startTime < timeoutMs) { const current = await this.readModelStore.get<UserProfile>('profiles', userId); if (current && current.version >= expectedVersion) { return current; } await sleep(pollInterval); } return null; }} // Strategy 2: Optimistic UI with confirmationinterface OptimisticUpdateResponse { success: boolean; // Return what the UI should show immediately optimisticState: Partial<UserProfile>; // Token to poll for confirmation confirmationToken: string; // Expected version after update expectedVersion: number;} class OptimisticUpdateHandler { async updateEmail( userId: string, newEmail: string ): Promise<OptimisticUpdateResponse> { // Execute command const result = await this.commandBus.send({ type: 'ChangeEmail', userId, newEmail, }); return { success: true, optimisticState: { email: newEmail }, confirmationToken: result.correlationId, expectedVersion: result.newVersion, }; }} // Frontend handlingasync function handleEmailChange(newEmail: string): Promise<void> { // 1. Show optimistic update immediately displayEmail(newEmail); showProcessingIndicator(); // 2. Send update const response = await api.updateEmail(userId, newEmail); // 3. Poll or subscribe for confirmation const confirmed = await waitForConfirmation( response.confirmationToken, response.expectedVersion, 5000 // timeout ); if (confirmed) { hideProcessingIndicator(); showSuccessToast('Email updated'); } else { // Fetch actual state and reconcile const actual = await api.getProfile(userId); displayEmail(actual.email); if (actual.email !== newEmail) { showWarningToast('Please verify your email change'); } }} // Strategy 3: Synchronous projections for critical pathsclass CriticalPathProjection { async handleWithImmediateConsistency( event: StoredEvent, transaction: Transaction ): Promise<void> { // Update projection in the same transaction as event write switch (event.eventType) { case 'PaymentReceived': await transaction.update('orders', event.aggregateId, { paymentStatus: 'paid', paidAt: event.timestamp, version: event.sequenceNumber, }); break; } }}Some domains have hard requirements for immediate consistency—banking balance checks, inventory reservations, reservation systems. For these cases, either use synchronous projections (accepting the coupling) or validate invariants on the aggregate itself, not on projections. Understand your domain's actual consistency requirements before assuming eventual is acceptable.
Events are immutable, but requirements evolve. What happens when you need to:
Unlike database migrations that transform data in place, event schema evolution must handle events written years ago alongside events written today.
The Schema Evolution Challenge
Your projection code that processes OrderPlaced events might receive:
All three versions must be handled correctly—forever.
| Strategy | When to Use | Tradeoffs |
|---|---|---|
| Strong Schema Versioning | Major breaking changes | More complex; explicit handling per version |
| Weak Schema Versioning | Minor additive changes | Optional fields; null handling throughout |
| Upcasting | Transform old events to new format on read | Centralized conversion; slight read overhead |
| Copy & Transform | Rare; when upcasting is insufficient | One-time migration script; creates new events |
| Event Replacement | Never for sourcing; sometimes for projections | Loses history; only for computed projections |
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/** * Event schema evolution patterns */ // Pattern 1: Upcasting - Transform old versions to new on readinterface EventUpcaster { eventType: string; fromVersion: number; toVersion: number; upcast(event: StoredEvent): StoredEvent;} class UpcasterRegistry { private upcasters = new Map<string, EventUpcaster[]>(); register(upcaster: EventUpcaster): void { const key = `${upcaster.eventType}:${upcaster.fromVersion}`; const existing = this.upcasters.get(key) ?? []; this.upcasters.set(key, [...existing, upcaster]); } upcast(event: StoredEvent): StoredEvent { const version = event.schemaVersion ?? 1; let current = event; let currentVersion = version; while (true) { const key = `${event.eventType}:${currentVersion}`; const upcasters = this.upcasters.get(key); if (!upcasters || upcasters.length === 0) { break; // No more upcasters } for (const upcaster of upcasters) { current = upcaster.upcast(current); currentVersion = upcaster.toVersion; } } return current; }} // Example upcastersconst orderPlacedV1ToV2: EventUpcaster = { eventType: 'OrderPlaced', fromVersion: 1, toVersion: 2, upcast(event) { // V1 had flat address fields; V2 has nested address object const data = event.data as OrderPlacedV1Data; return { ...event, schemaVersion: 2, data: { ...data, // Convert flat fields to nested structure shippingAddress: { street: data.shippingStreet ?? '', city: data.shippingCity ?? '', postalCode: data.shippingZip ?? '', country: data.shippingCountry ?? 'US', }, // Remove old flat fields (or keep for compatibility) } as OrderPlacedV2Data, }; },}; const orderPlacedV2ToV3: EventUpcaster = { eventType: 'OrderPlaced', fromVersion: 2, toVersion: 3, upcast(event) { // V3 added giftOptions const data = event.data as OrderPlacedV2Data; return { ...event, schemaVersion: 3, data: { ...data, giftOptions: { isGift: false, wrapStyle: null, giftMessage: null, }, } as OrderPlacedV3Data, }; },}; // Pattern 2: Defensive deserializationfunction deserializeOrderPlaced(event: StoredEvent): OrderPlacedData { const raw = event.data as Record<string, unknown>; const version = event.schemaVersion ?? 1; // Always return consistent shape, regardless of version return { orderId: raw.orderId as string, customerId: raw.customerId as string, items: raw.items as OrderItem[], total: raw.total as number, // Handle fields that may be missing in old versions shippingAddress: raw.shippingAddress ? raw.shippingAddress as Address : { // Reconstruct from old flat fields if present street: (raw.shippingStreet as string) ?? '', city: (raw.shippingCity as string) ?? '', postalCode: (raw.shippingZip as string) ?? '', country: (raw.shippingCountry as string) ?? 'UNKNOWN', }, giftOptions: raw.giftOptions as GiftOptions | undefined, currency: (raw.currency as string) ?? 'USD', // Defaulted field createdAt: new Date(raw.createdAt as string), };} // Pattern 3: Typed event handlers with version awarenessclass VersionAwareProjection { handle(event: StoredEvent): void { // Upcast to latest version const upcasted = this.upcasterRegistry.upcast(event); switch (event.eventType) { case 'OrderPlaced': this.handleOrderPlaced(deserializeOrderPlaced(upcasted)); break; // ... other handlers } } private handleOrderPlaced(data: OrderPlacedData): void { // Always works with latest schema shape // No version checks in business logic }}Best practices: (1) Make new fields optional with sensible defaults, (2) Never remove fields from serialization, just stop using them, (3) Include schema version with every event, (4) Centralize upcasting logic, (5) Test projections against historical event samples. Schema evolution is inevitable—plan for it from day one.
Event stores grow forever. Unlike CRUD systems where deletion removes data, event sourcing only appends. Over years, event stores can reach hundreds of gigabytes or terabytes.
The Storage Growth Problem
The Replay Performance Problem
Long-running aggregates that accumulate thousands of events become slow to reconstitute. A user account with 10 years of activity might have 50,000 events—replaying that for every command is unacceptable.
| Metric | Small System | Medium System | Large System |
|---|---|---|---|
| Events/day | 10K | 1M | 100M+ |
| Event size (avg) | 1 KB | 1 KB | 500 B - 2 KB |
| Daily growth | 10 MB | 1 GB | 50-200 GB |
| Annual growth | 3.6 GB | 365 GB | 18-73 TB |
| Archival needed | No | Maybe | Yes |
| Snapshot strategy | Optional | Recommended | Essential |
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/** * Storage management strategies */ // Strategy 1: Tiered storage with archivalclass TieredEventStore { constructor( private hotStore: EventStore, // Fast SSD, recent events private warmStore: EventStore, // Standard storage, older events private coldStore: ArchivalStore, // S3/Glacier, archived events private config: TieringConfig ) {} async readStream( streamId: string, options?: ReadOptions ): Promise<ReadResult> { // Try hot store first const hotEvents = await this.hotStore.readStream(streamId, options); if (this.coversFullHistory(hotEvents, streamId)) { return hotEvents; } // Need to fetch from warm/cold storage const warmEvents = await this.warmStore.readStream(streamId, { ...options, toPosition: hotEvents.events[0]?.sequenceNumber ?? Number.MAX_SAFE_INTEGER, }); return { events: [...warmEvents.events, ...hotEvents.events], version: hotEvents.version, }; } // Background job: move old events to cooler storage async archiveOldEvents(): Promise<void> { const cutoffDate = new Date(); cutoffDate.setDate(cutoffDate.getDate() - this.config.hotRetentionDays); const eventsToArchive = await this.hotStore.readAll({ toTimestamp: cutoffDate, count: 10000, }); // Move to warm store await this.warmStore.appendBatch(eventsToArchive.events); // Mark as archived in hot store (or delete if warm is durable) await this.hotStore.markArchived(eventsToArchive.events.map(e => e.eventId)); }} // Strategy 2: Aggregate-aware snapshottingclass AdaptiveSnapshotting { constructor( private snapshotStore: SnapshotStore, private metricsService: MetricsService ) {} async shouldSnapshot( aggregateId: string, eventCount: number, lastSnapshotAge: number ): Promise<boolean> { // Dynamic thresholds based on aggregate access patterns const accessFrequency = await this.metricsService.getAccessFrequency(aggregateId); // High-traffic aggregates: snapshot more frequently if (accessFrequency > 100) { // 100 reads/hour return eventCount >= 20 || lastSnapshotAge > 3600000; // 1 hour } // Medium traffic if (accessFrequency > 10) { return eventCount >= 50 || lastSnapshotAge > 86400000; // 1 day } // Low traffic: standard snapshotting return eventCount >= 100; }} // Strategy 3: Event compaction (use with caution!)interface CompactionConfig { // Only compact events older than this minAge: number; // Compact if more than this many events minEvents: number; // Event types that are safe to compact compactableTypes: string[];} class EventCompaction { /** * CAUTION: Compaction loses granularity! * Only use for specific, well-understood scenarios * (e.g., summarizing daily metrics into weekly) */ async compact( streamId: string, config: CompactionConfig ): Promise<void> { const events = await this.eventStore.readStream(streamId); // Find events eligible for compaction const cutoffDate = new Date(Date.now() - config.minAge); const oldEvents = events.events.filter( e => e.timestamp < cutoffDate && config.compactableTypes.includes(e.eventType) ); if (oldEvents.length < config.minEvents) { return; // Not enough to compact } // Create summary event const summary = this.summarizeEvents(oldEvents); // Write summary to new stream (preserving original) await this.eventStore.append( `${streamId}:compacted`, 'any', [summary] ); console.log( `Compacted ${oldEvents.length} events into 1 summary for ${streamId}` ); }}Event sourcing is a significant architectural commitment. It's not appropriate for every system. Here's a framework for deciding whether it fits your context.
Strong Indicators FOR Event Sourcing:
| Factor | Weight | Pro-ES | Anti-ES |
|---|---|---|---|
| Audit/Compliance | High | Required by regulation | No audit requirements |
| Temporal Queries | Medium | Core business need | Rarely needed |
| Integration Complexity | Medium | Many downstream consumers | Few/no integrations |
| Team Experience | High | Prior ES experience | First ES project |
| Domain Fit | Medium | Event-oriented domain | Simple CRUD entities |
| Consistency Model | High | Eventual OK | Must be immediate |
If you're unsure, consider applying event sourcing to a single bounded context first—ideally one with strong audit requirements and clear event boundaries. Validate your team can operate it before expanding. Event sourcing in one service doesn't require it in all services.
We've provided an honest assessment of event sourcing—acknowledging both its transformative benefits and its real operational challenges. Let's consolidate:
What's Next:
With a clear understanding of benefits and challenges, the final page examines When to Use Event Sourcing—providing concrete guidelines, decision frameworks, and real-world examples to help you decide if event sourcing is right for your next project.
You now have an honest, balanced view of event sourcing's tradeoffs. The benefits—audit trails, debugging, integration—are real and transformative for the right use cases. The challenges—eventual consistency, schema evolution, storage growth—are manageable but require deliberate engineering. Next, we'll provide concrete guidance for the decision: when should you actually use event sourcing?