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Quick Definition

Event Storming is a collaborative modeling workshop technique focused on discovering, mapping, and defining domain events that drive system behavior across teams and architectures.

Analogy: Event Storming is like a mapping expedition where domain events are landmarks, stakeholders are scouts, and the resulting map guides system design and operations.

Formal technical line: Event Storming enumerates domain events and their causal relationships to create an executable model of business behavior useful for bounded contexts, domain-driven design, and system integration.

If Event Storming has multiple meanings, the most common meaning is the workshop and collaborative modeling method used in Domain-Driven Design. Other meanings include:

• A lightweight discovery technique for event-driven architecture design.
• A microservices alignment practice for identifying bounded contexts and integration points.
• A facilitation approach used in product discovery and incident postmortems.

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What is Event Storming?

What it is:

• A facilitated, cross-disciplinary workshop that surfaces domain events, commands, aggregates, processes, and policies using colored sticky notes or virtual equivalents.
• A means to align product, design, engineering, and operations on the behavior that matters.
• A discovery-first approach that starts with events as the primary truth and then builds services, data models, and integrations around them.

What it is NOT:

• Not a formal specification language by itself.
• Not a replacement for detailed API contracts, schemas, or infra configs.
• Not a one-off mapping exercise; it is iterative and should inform design, tests, and observability.

Key properties and constraints:

• Event-centric: uses events as first-class artifacts.
• Collaborative: requires domain experts and technical stakeholders.
• Visual and temporal: emphasizes event sequences and time ordering.
• Emergent architecture: architectures are derived rather than imposed.
• Bounded by context: works best when scope is clear and stakeholders committed.
• Non-prescriptive on implementation: can lead to event-driven, request-response, or hybrid systems.

Where it fits in modern cloud/SRE workflows:

• Early-phase architecture and product discovery for cloud-native systems.
• Pre-integration mapping before designing pub/sub, streams, or service meshes.
• Inputs for SRE artifacts: SLIs, SLOs, error budgets, runbooks, and incident playbooks.
• Helps define telemetry needs and trace boundaries for distributed tracing and observability.

Diagram description readers can visualize:

• Imagine a long timeline on a wall. Events are sticky notes in chronological order. Commands, policies, and actors are placed above. Read models and storage are below. Arrows show causation and integrations to external systems. Colors encode types: domain event, command, saga/process, actor, read model, external system.

Event Storming in one sentence

A facilitated workshop method that captures domain events and their relationships to align teams and drive event-driven design, telemetry, and operational controls.

Event Storming vs related terms (TABLE REQUIRED)

ID	Term	How it differs from Event Storming	Common confusion
T1	Domain-Driven Design	Focuses on strategic modeling beyond just events	Often treated as identical
T2	Event-Driven Architecture	Architecture approach, not the workshop process	Workshop does not mandate EDA
T3	CQRS	Architectural pattern for read/write separation	Event Storming is discovery not implementation
T4	Event Sourcing	Storage pattern recording events as state	Storming identifies events, not storage choice
T5	Process Modeling	Often formal BPMN diagrams and rules	Storming is exploratory and domain-led
T6	Systems Thinking	Broader discipline about whole systems	Storming is a specific facilitation technique
T7	Story Mapping	Prioritizes user journeys and features	Storming centers on domain events not UI tasks
T8	Impact Mapping	Strategic alignment with objectives	Storming focuses on event flows not goals

Row Details (only if any cell says “See details below”)

• None

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Why does Event Storming matter?

Business impact:

• Clarifies revenue-impacting events such as order placed, subscription renewed, or payment failed.
• Reduces time-to-market by aligning stakeholders earlier and cutting rework caused by misunderstood workflows.
• Helps quantify and reduce business risk by exposing critical failure points and external dependencies.

Engineering impact:

• Helps reduce incidents by making event flows explicit for tracing and resilience design.
• Increases delivery velocity by surfacing integration contracts and bounded contexts before implementation.
• Improves testability because event scenarios produce clear acceptance and integration tests.

SRE framing:

• SLIs and SLOs can be derived from domain events (e.g., order processed within X seconds).
• Error budgets allocated against business-impacting events rather than low-level infra metrics only.
• Toil reduction by automating runbooks related to event handling and retry logic.
• On-call clarity through mapping: who owns which events and which services handle retries or compensations.

What commonly breaks in production (realistic examples):

• Downstream consumer misses events due to lack of backpressure handling, causing data loss or inconsistency.
• Long-running sagas time out because of missing heartbeats or retry policies.
• Unknown external dependency outages cause silent failures because no telemetry on event delivery exists.
• Schema changes cause consumer deserialization errors, leading to backlogs and incident pages.
• Race conditions in event ordering cause duplicated or inconsistent business state.

Use practical language: these failures often occur when teams lack shared event definitions, observability, or resilient delivery patterns.

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Where is Event Storming used? (TABLE REQUIRED)

ID	Layer/Area	How Event Storming appears	Typical telemetry	Common tools
L1	Edge and API	Maps incoming requests to events and validation	Request latency, error rates, traces	API gateways, ingress
L2	Service and Business Logic	Defines service boundaries and event producers	Processing time, queue depth, error counts	Message brokers, microservices
L3	Data and Storage	Shows event-derived read models and eventual consistency	Replication lag, stale-read rates	Databases, CDC tools
L4	Integration and External Systems	Identifies external calls and compensation flows	External latency, failure rates	Pubsub, HTTP clients
L5	Cloud Platform	Guides service provisioning and scaling knobs	Autoscale events, resource utilization	Kubernetes, serverless
L6	CI/CD and Delivery	Drives pipeline triggers and schema validation	Deploy success, rollback count	CI pipelines, lint tools
L7	Observability and Ops	Informs trace spans and SLI definitions	Trace coverage, alert counts	Tracing, metrics, logging
L8	Security and Compliance	Maps events that imply audit or PII handling	Audit logs, access errors	SIEMs, IAM

Row Details (only if needed)

• None

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When should you use Event Storming?

When it’s necessary:

• At discovery for new product features or domains with unclear rules.
• When integrating across multiple teams or legacy systems to avoid hidden coupling.
• When designing event-driven or hybrid architectures that require clear contracts.

When it’s optional:

• For small, single-service features with low cross-team impact.
• When a mature domain model and tests already exist and stakeholders agree.

When NOT to use / overuse it:

• Avoid deep Event Storming for trivial UI tweaks or single-line data fixes.
• Do not treat a single workshop as the final answer; iterative refinement is required.
• Refrain from using it as a replacement for engineering design docs or security threat modeling.

Decision checklist:

• If multiple teams and asynchronous flows -> do Event Storming.
• If single-author CRUD change with no integrations -> optional.
• If unknown external dependencies and regulatory concerns -> do Event Storming plus compliance review.
• If high throughput, low-latency domain -> combine Event Storming with performance design sessions.

Maturity ladder:

• Beginner: Short 2–4 hour workshop, map primary domain events, identify actors.
• Intermediate: Full-day session, identify commands, aggregates, read models, and basic sagas.
• Advanced: Multi-day cross-team modeling, derive schemas, contracts, test harnesses, and telemetry requirements with automation.

Example decisions:

• Small team: If the feature touches one service and has no external consumers -> light Event Storming with developer and product owner for 60–90 minutes.
• Large enterprise: If the initiative spans billing, inventory, and customer services -> multi-day Event Storming across 10+ stakeholders and dedicated facilitator, followed by automated contract generation and telemetry plan.

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How does Event Storming work?

Step-by-step overview:

1. Prepare: Define scope and invite domain experts, product managers, architects, SRE, and integrators.
2. Materials and tools: physical sticky notes of different colors or an online collaboration board with color types.
3. Start with domain events: Ask domain experts “what happened?” and place events in chronological order.
4. Identify actors and commands: Above events, place commands and who issued them.
5. Add aggregates and policies: Map entities responsible for state and add business policies that cause events.
6. Discover processes/sagas: Connect related events into processes or long-running transactions.
7. Identify read models and projections: What information consumers need and where it is stored.
8. Find external systems and failure modes: Place external systems and note dependencies and compensations.
9. Capture open questions and decisions: Flag scenarios needing further analysis or technical constraints.
10. Convert to artifacts: Create event schemas, contract tests, telemetry plan, and runbooks.

Components and workflow:

• Domain events: immutable facts that occurred.
• Commands: intent messages that trigger actions.
• Aggregates: boundaries of consistency and transaction.
• Policies/processes: automated reactions to events (sagas).
• External systems: third-party services that produce or consume events.
• Read models: optimized views for queries.

Data flow and lifecycle:

• User or external actor issues a command -> Command validated -> Domain event emitted -> Consumers react and update read models -> Side effects executed or external calls invoked -> Saga may emit compensating actions on failure.

Edge cases and failure modes:

• Out-of-order events: caused by at-least-once delivery or partitioning.
• Duplicate events: lack of idempotency leads to incorrect state.
• Schema evolution: incompatible changes break consumers.
• Long-running sagas across deployments: orchestration state lost during upgrade.
• Backpressure: consumers unable to keep up causing queue growth.

Short practical example pseudocode (not in table):

• Publish event example:
• event = {id, type: “OrderPlaced”, timestamp, payload}
• publish(“orders”, event)
• Consumer pseudocode:
• subscribe(“orders”, handler)
• handler(event) { if processed(event.id) return; processOrder(event.payload); markProcessed(event.id) }

Typical architecture patterns for Event Storming

• Event-First Microservices: services own events and contracts; use for loosely coupled, independent teams.
• CQRS with Event Sourcing: append-only events become the source of truth; use for auditability and complex business logic.
• Choreography-based Sagas: services react to events for eventual consistency; use to avoid central orchestrator and reduce coupling.
• Orchestration-based Sagas: a central coordinator manages the flow; use when a single workflow needs centralized control and transaction semantics.
• Hybrid: combine sync APIs for critical low-latency interactions and events for asynchronous workflows.

Failure modes & mitigation (TABLE REQUIRED)

ID	Failure mode	Symptom	Likely cause	Mitigation	Observability signal
F1	Event loss	Missing business updates	No durable publish or ack loss	Add durable broker and retries	Missing event counts
F2	Duplicate processing	Duplicate side effects	At-least-once without idempotency	Implement idempotency keys	Duplicate transaction IDs
F3	Schema mismatch	Consumer errors	Incompatible schema change	Use schema registry and versioning	Deserialization errors
F4	Ordering violation	Inconsistent state	Partitioned consumers or unordered delivery	Add sequence numbers or ordering keys	Out-of-order sequence gaps
F5	Backpressure	Queue growth and latency	Slow consumers or bursts	Add buffering and scaling	Queue depth and processing latency
F6	Long saga timeout	Incomplete workflows	Missing heartbeats or retries	Implement timeouts and compensations	Stale saga instances
F7	Invisible failures	No alerts on delivery failures	Missing telemetry on broker errors	Instrument delivery and errors	Broker error rate
F8	Unauthorized events	Security incidents	Weak authentication between services	Enforce mTLS and IAM	Unauthorized access logs

Row Details (only if needed)

• None

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Key Concepts, Keywords & Terminology for Event Storming

Term — definition — why it matters — common pitfall

Event — An immutable record of something that has happened — Central artifact of Event Storming — Treated as a command mistakenly
Domain Event — Business-level event with domain meaning — Drives business workflows — Overloading with technical logs
Command — An intent to perform an action — Shows who initiated change — Confused with events
Aggregate — Consistency boundary for state changes — Guides transactional design — Aggregates too large cause contention
Saga — Long-running process that manages multi-step transactions — Handles eventual consistency — Lack of compensations
Policy — Automated rule reacting to events — Encodes business rules — Hidden policies cause surprises
Read Model — Query-optimized projection of state — Improves performance and UX — Not updated atomically with write model
Projection — Transformation from events to read model — Enables quick lookups — Stale projections if not monitored
Idempotency — Ability to handle duplicate events safely — Prevents duplicate side effects — Not implemented consistently
Event Schema — Structured format of an event payload — Enables contracts and compatibility — Breaking changes cause outages
Schema Registry — Central store for event schemas and versions — Helps enforce compatibility — Underused in small teams
Event Broker — Middleware that routes events between producers and consumers — Scalable delivery primitive — Single point of failure if misconfigured
Pub/Sub — Messaging pattern producing asynchronous decoupling — Simplifies integrations — Misused for synchronous needs
Partition Key — Key used to route related events to same partition — Preserves ordering — Poor partitioning causes hotspots
Sequence Number — Ordering identifier inside a partition — Detects out-of-order events — Not exposed across partitions
Compensation — Action that reverses or mitigates a previous action — Enables failure recovery — Not defined for all sagas
Choreography — Decentralized saga pattern using events — Reduces central coupling — Hard to reason about in complex flows
Orchestration — Central controller pattern for saga workflows — Easier to manage complex flows — Central orchestrator can be a bottleneck
Event Sourcing — Persisting state as a sequence of events — Full audit trail and rebuildability — Storage costs and query complexity
CQRS — Command Query Responsibility Segregation splitting reads and writes — Allows independent scaling — Increased system complexity
Bounded Context — Domain partition where terms have specific meaning — Prevents model leaks — Boundaries are often fuzzy
Ubiquitous Language — Shared terminology among stakeholders — Prevents miscommunication — Not enforced across teams
Time-ordered log — Events arranged by time — Useful for replay and debugging — Clock skew complicates ordering
At-least-once — Delivery guarantee that can cause duplicates — Safer delivery than at-most-once — Requires idempotency handling
At-most-once — Delivery guarantee that may drop events — Simpler semantics — Risky for critical events
Exactly-once — Ideal delivery semantics often hard to achieve — Simplifies consumer logic — Expensive and not always needed
Event Replay — Reprocessing historical events to rebuild state — Useful for migrations and repairs — Replays can cause side effects if not idempotent
Backpressure — System throttling when consumers lag — Prevents collapse of systems — Often not planned early enough
Dead-letter queue — Place for failed events for later inspection — Avoids lost events — Can be ignored and grow unbounded
Contract Testing — Tests that verify producers and consumers agree on schemas — Prevents integration failures — Requires test infra and maintenance
Observability Contract — Defined telemetry required for events — Ensures production visibility — Often missing for new events
Trace Context — Distributed tracing metadata passed with events or calls — Enables end-to-end debugging — Not forwarded across async hops
Correlation ID — Identifier linking related operations — Critical for incident debugging — Not consistently propagated
Idempotency Key — Unique ID to dedupe processing — Short term dedupe solution — Needs lifecycle management
Event Versioning — Strategy for evolving event schemas — Enables backward compatibility — Poor versioning breaks consumers
Compensating Transaction — Specific rollback action in saga — Restores consistency — Under-specified compensations cause leaks
Event Consumer — Service processing events — Implements business logic — Lacks retries or monitoring often
Event Producer — Service emitting events — Owns the schema and enlistment — Assumes consumers will handle changes
Event Registry — Catalog of events and contracts — Facilitates discovery — Must be kept up to date
Audit Trail — Immutable sequence used for compliance — Important for regulation — Large storage and privacy concerns
Event-driven Testing — Testing patterns for event flows and eventual consistency — Prevents regressions — Hard to simulate at scale
Latency Budget — Acceptable delay for event propagation — Informs SLOs and UX — Often undefined
Retry Policy — Rules for retrying failed processing — Improves robustness — Can amplify load if naive
Compensation Queue — Queue for delayed compensations — Avoids blocking main flow — Needs monitoring
Actor — The human or system that initiates commands — Clarifies ownership — Misidentified actors confuse models
Event Catalog — Indexed list of domain events with intent — Reduces duplicated events — Often informal and unsearchable

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How to Measure Event Storming (Metrics, SLIs, SLOs) (TABLE REQUIRED)

ID	Metric/SLI	What it tells you	How to measure	Starting target	Gotchas
M1	Event Delivery Success Rate	Percent of events successfully delivered	delivered_events / published_events	99.9% for business-critical	Counts may double if dedupe missing
M2	End-to-end Event Latency	Time from event publish to consumer processing	P95 of processing_time per event	P95 < 1s for low-latency domains	Clock sync and async hops affect measure
M3	Event Processing Error Rate	Rate of consumer processing failures	failed_processes / processed_events	< 0.1% initial	Retries may mask underlying failures
M4	Queue Depth	Backlog indicating consumer lag	items_in_queue over time	Maintain below 50% of retention window	Spikes during deploys inflate depth
M5	Replay Duration	Time to fully replay a timeframe	time_to_replay / events_replayed	Acceptable within maintenance window	Replays can trigger side effects
M6	Schema Compatibility Failures	Consumers failing due to schema issues	schema_errors per deploy	Zero for backward incompatible deploys	Registry blind spots cause surprises
M7	Saga Completion Rate	Successful saga completions vs starts	completed_sagas / started_sagas	99% for critical flows	Long-running sagas distort rate
M8	Dead-letter Rate	Events moved to DLQ per total	dlq_events / total_events	< 0.1%	DLQ growth hides root issues
M9	Trace Coverage	Percent of events with trace context	traced_events / published_events	90%	Async hops drop context often
M10	Time to Detect Delivery Failure	Time from failure to alert	median detection_seconds	< 5 minutes for critical events	Noise in alerts delays triage

Row Details (only if needed)

• None

Best tools to measure Event Storming

Tool — Tracing system

• What it measures for Event Storming: End-to-end latency and causal chains.
• Best-fit environment: Microservices and distributed systems.
• Setup outline:
• Instrument producers and consumers with trace propagation.
• Add trace context to event headers.
• Configure sampling and retention.
• Build trace queries for event IDs and correlation IDs.
• Integrate traces into alerting and runbooks.
• Strengths:
• Visual causal chains.
• Good for debugging cross-service flows.
• Limitations:
• Sampling may miss rare failures.
• Async context propagation can be tricky.

Tool — Metrics system

• What it measures for Event Storming: SLIs like success rates and latencies.
• Best-fit environment: Any service emitting metrics.
• Setup outline:
• Expose counters and histograms for publish, consume, errors.
• Tag metrics with event type and environment.
• Create SLO dashboards and alerts.
• Strengths:
• Aggregation and alerting.
• Low overhead.
• Limitations:
• Lack of causal detail for complex flows.

Tool — Log aggregation

• What it measures for Event Storming: Detailed event payload logs and errors.
• Best-fit environment: Services with structured logging.
• Setup outline:
• Standardize log fields for event id, type, correlation id.
• Ship logs to central store with retention policy.
• Create queries for failed events and DLQ items.
• Strengths:
• Full fidelity for audits and postmortems.
• Limitations:
• High storage and query costs.

Tool — Schema registry

• What it measures for Event Storming: Schema versions and compatibility checks.
• Best-fit environment: Teams using structured events with enforced schemas.
• Setup outline:
• Register schemas and enforce compatibility rules.
• Integrate with CI to block incompatible changes.
• Provide discovery UI for events.
• Strengths:
• Prevents breaking changes.
• Limitations:
• Requires cultural adoption.

Tool — Broker monitoring

• What it measures for Event Storming: Broker health, queue depth, partition skew.
• Best-fit environment: Kafka, managed pubsub, or queues.
• Setup outline:
• Instrument broker metrics exporter.
• Create consumer lag dashboards.
• Alert on partition imbalance.
• Strengths:
• Early signs of system stress.
• Limitations:
• Broker metrics need correlation with business events.

Recommended dashboards & alerts for Event Storming

Executive dashboard:

• Panels: Business events per minute, success rate trend, SLA burn rate, outstanding DLQ count.
• Why: High-level view of customer-impacting metrics and trends for leadership.

On-call dashboard:

• Panels: Failed event types, queue depth by service, slowest consumers, recent error logs.
• Why: Fast triage of production issues and prioritization of paging.

Debug dashboard:

• Panels: Trace timeline for selected event ID, consumer processing durations, retry counts, schema error logs.
• Why: Deep troubleshooting to identify root cause and recovery path.

Alerting guidance:

• Page vs ticket: Page for SLO breaches that impact customers and require immediate human action; create ticket for degradations that are non-urgent.
• Burn-rate guidance: Use error budget burn rate alerts to escalate; e.g., notify on 50% burn in one hour and page on 100% in 15 minutes for critical SLOs.
• Noise reduction tactics: Deduplicate alerts by grouping by root cause, suppress low-value alerts during planned deploys, use rolling windows and thresholds, and employ correlation IDs to cluster.

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Implementation Guide (Step-by-step)

1) Prerequisites – Stakeholder alignment and invite list. – Defined scope and objectives for the workshop. – Collaboration tools or physical supplies. – Baseline observability: metrics, traces, and logging.

2) Instrumentation plan – Identify events to instrument and required metadata (id, type, timestamp, correlation id, trace context). – Define metric names and tags for publish, consume, errors. – Decide on schema registry and compatibility rules.

3) Data collection – Standardize logging fields and ship logs to central store. – Emit metrics at producers and consumers. – Ensure trace context propagation with async headers.

4) SLO design – Use business events to define SLIs: delivery success, processing latency. – Set realistic SLOs based on historical data and business impact.

5) Dashboards – Build executive, on-call, and debug dashboards. – Add per-event-type views and cross-service dependency panels.

6) Alerts & routing – Map alerts to on-call teams and escalation policies. – Use automation to suppress planned maintenance notifications.

7) Runbooks & automation – Create runbooks for common failures like schema incompatibility or DLQ growth. – Automate mitigation where safe: auto-retries with backoff, consumer auto-scaling.

8) Validation (load/chaos/game days) – Run replay and load tests to validate throughput and latency. – Perform chaos experiments on brokers and consumers. – Execute game days simulating critical event loss or backpressure.

9) Continuous improvement – Capture learnings from incidents into the event catalog. – Regularly revisit event definitions and telemetry.

Checklists

Pre-production checklist:

• Events defined with schema and example payloads.
• Metrics emitted for publish/consume/errors.
• Trace context added to events.
• Schema registered and compatibility rules set.
• DLQ and retry policy defined.

Production readiness checklist:

• SLOs configured and dashboards live.
• Alerts mapped and runbooks written.
• Consumer autoscaling tested.
• Backpressure and retention policies configured.
• Access control and encryption configured.

Incident checklist specific to Event Storming:

• Identify affected event types and time window.
• Correlate traces and logs by correlation ID.
• Check broker metrics and DLQ contents.
• Determine whether to replay events or apply compensations.
• Notify stakeholders and update incident timeline.

Examples for Kubernetes and managed cloud service:

• Kubernetes example: Verify sidecar injection for tracing, check consumer pod HorizontalPodAutoscaler, ensure ConfigMap contains schema registry endpoint, verify ServiceAccount RBAC for broker access.
• Managed cloud service example: Confirm IAM role for pubsub topics, enable managed DLQ and retention, configure managed tracing sampling, verify subscription push/backoff policies.

What “good” looks like:

• Low DLQ rate, trace coverage above 90%, SLOs meeting targets, runbooks produce consistent recovery steps.

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Use Cases of Event Storming

1. Billing reconciliation across legacy systems – Context: Multiple billing backends aggregated into one invoice. – Problem: Missing or duplicated charges due to inconsistent integrations. – Why Event Storming helps: Surface event boundaries and compensations. – What to measure: Invoice generation latency, reconciliation failures. – Typical tools: Message broker, CDC, schema registry.

2. Order processing with inventory and shipping – Context: E-commerce order needs inventory hold and shipping booking. – Problem: Inventory oversell and shipping failures during peak. – Why Event Storming helps: Identify sagas and compensation for payment refunds. – What to measure: Saga completion rate, time to ship, DLQ rate. – Typical tools: Pub/sub, orchestration service, tracing.

3. Feature rollout with event-driven feature flags – Context: Gradual feature enablement with event migrations. – Problem: Inconsistent behavior across consumers after rollout. – Why Event Storming helps: Map events to flag states and migration sequences. – What to measure: Event schema compatibility failures and error rate during rollout. – Typical tools: Feature flag service, schema registry.

4. Real-time analytics pipeline – Context: Streaming events into analytics for dashboards. – Problem: Latency and missing data skew analytics. – Why Event Storming helps: Define essential events and SLIs for freshness. – What to measure: Event lag, processing throughput. – Typical tools: Stream processing, data warehouse.

5. Fraud detection integration – Context: Events from transactions used for ML scoring. – Problem: Missing attributes and delayed events reduce detection accuracy. – Why Event Storming helps: Ensure required telemetry and enrichment steps. – What to measure: Time to score, missing feature counts. – Typical tools: Message broker, feature store, ML scoring service.

6. Customer notification system – Context: Events trigger email and SMS notifications. – Problem: Duplicate notifications and compliance issues. – Why Event Storming helps: Map idempotency keys and consent policies. – What to measure: Delivery success, duplicate notification rate. – Typical tools: Notification service, DLQ.

7. Multi-region failover – Context: Global service needs consistent events across regions. – Problem: Conflicting events and divergence after failover. – Why Event Storming helps: Define ordering keys and replication boundaries. – What to measure: Replication lag, reconciliation errors. – Typical tools: Global message broker, CDC.

8. Compliance audit trail – Context: Regulatory requirement to store auditable events. – Problem: Missing audit data and retention non-compliance. – Why Event Storming helps: Identify audit-relevant events and retention policies. – What to measure: Audit event completeness and retention adherence. – Typical tools: Append-only store, immutable logs.

9. CI/CD contract testing – Context: Many services emit and consume events across teams. – Problem: Integration regressions after deploys. – Why Event Storming helps: Extract contracts and design consumer tests. – What to measure: Contract test failures, deploy rollback rates. – Typical tools: Contract testing frameworks, CI pipelines.

10. Incident postmortem synthesis – Context: Production outage caused inconsistent state. – Problem: Hard to trace root cause across async flows. – Why Event Storming helps: Reconstruct event timeline and responsibilities. – What to measure: Time to repair, recurrence rate. – Typical tools: Tracing, logs, event catalog.

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Scenario Examples (Realistic, End-to-End)

Scenario #1 — Kubernetes: High-throughput order processing

Context: E-commerce platform using Kubernetes with multiple microservices for orders, inventory, and payments.
Goal: Ensure orders are processed reliably under peak traffic while keeping latency within acceptable bounds.
Why Event Storming matters here: Align teams on event boundaries, design sagas for payments and inventory, and define telemetry for autoscaling.
Architecture / workflow: Orders API -> Order service publishes OrderPlaced -> Inventory service reserves stock -> Payment service charges -> OrderConfirmed event -> Shipping queued.
Step-by-step implementation: Run a 1-day Event Storming to map events and sagas; create event schemas; instrument publish/consume metrics; configure HPA based on consumer lag; add idempotency keys; register schemas.
What to measure: Queue depth, delivery success rate, P95 end-to-end latency, saga completion.
Tools to use and why: Kubernetes for deployment control; broker with partitioning for throughput; tracing for end-to-end visibility.
Common pitfalls: Partition hot spots causing ordering issues; missing idempotency; insufficient broker retention.
Validation: Load test at 2x peak, simulate consumer failure, verify replay and compensations.
Outcome: Predictable scaling and faster incident resolution with clear ownership.

Scenario #2 — Serverless / managed-PaaS: Notification pipeline

Context: SaaS product using managed pubsub and serverless functions to send notifications.
Goal: Deliver notifications reliably while minimizing cost.
Why Event Storming matters here: Map event types that trigger notifications and define required metadata for personalization.
Architecture / workflow: Event producers -> managed pubsub topic -> serverless subscribers -> third-party SMS/email.
Step-by-step implementation: Host a half-day Event Storming; define event types and DLQ policies; add schema registry and contract tests in CI; instrument metrics and set SLO for delivery latency.
What to measure: Notification delivery success, DLQ rate, retry counts, cost per 1k notifications.
Tools to use and why: Managed pubsub for scaling, serverless for pay-per-use cost model, logging for audit.
Common pitfalls: Cold start spikes causing delays; insufficient batched sends increasing cost.
Validation: Chaos test simulated third-party provider failure and DLQ handling.
Outcome: Reduced cost with reliable delivery and clear compensation strategy.

Scenario #3 — Incident-response/postmortem: Payment outage

Context: Payment provider outage causing partial failures for order completions.
Goal: Restore consistency and prevent future recurrence.
Why Event Storming matters here: Recreate event timeline, identify missing compensations, and update runbooks.
Architecture / workflow: Orders publish events, payments attempted, failures recorded in DLQ.
Step-by-step implementation: Use Event Storming to map sequence and responsibility; collect traces and logs for timeframe; replay retryable events; execute compensations for partial charges.
What to measure: Time to detect outage, saga completion gap, number of affected customers.
Tools to use and why: Tracing, DLQ inspection tools, replay tools.
Common pitfalls: Replaying events causing duplicate charges if idempotency missing.
Validation: Dry-run replay on staging with simulated payment provider.
Outcome: Updated runbooks, added compensating transactions, improved monitoring.

Scenario #4 — Cost/performance trade-off: Analytics streaming

Context: Real-time analytics pipeline expensive at 1:1 event level.
Goal: Reduce cost while keeping analytics freshness within business needs.
Why Event Storming matters here: Identify which events are critical for real-time analytics versus batch.
Architecture / workflow: Producers emit all events -> router filters critical events to real-time stream -> others to batch processing.
Step-by-step implementation: Event Storming to categorize events; implement stream filters and sampling; define SLOs for freshness per category.
What to measure: Cost per event, latency for critical events, sampling error rates.
Tools to use and why: Stream processing for critical, batch ETL for non-critical.
Common pitfalls: Sampling introduces bias if not stratified.
Validation: Compare dashboard metrics pre and post change for accuracy drift.
Outcome: Lower processing costs and preserved business-critical freshness.

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Common Mistakes, Anti-patterns, and Troubleshooting

List of mistakes with symptom -> root cause -> fix (selected 20):

1. Symptom: DLQ rapidly grows -> Root cause: Unhandled schema change -> Fix: Register schema, enforce compatibility, add contract tests.
2. Symptom: Duplicate side effects -> Root cause: At-least-once delivery without idempotency -> Fix: Implement idempotency keys and dedupe store.
3. Symptom: Unclear ownership -> Root cause: Missing bounded context mapping -> Fix: Redefine bounded contexts in Event Storming and assign owners.
4. Symptom: High consumer lag -> Root cause: Underprovisioned consumers -> Fix: Autoscale consumers based on lag metrics.
5. Symptom: Missing trace chains -> Root cause: Trace context not propagated in events -> Fix: Add trace headers to event metadata.
6. Symptom: Out-of-order state -> Root cause: Poor partition key selection -> Fix: Choose partition keys for ordering or add sequence numbers.
7. Symptom: Replay causes duplicate actions -> Root cause: Replays not idempotent -> Fix: Add idempotency and replay-safe semantics.
8. Symptom: No alerts for broker failure -> Root cause: Lack of broker health telemetry -> Fix: Instrument broker metrics and set alerts.
9. Symptom: Unexpected behavior after deploy -> Root cause: No contract tests in CI -> Fix: Add consumer-driven contract tests in pipeline.
10. Symptom: Slow incident triage -> Root cause: Missing correlation IDs -> Fix: Enforce correlation ID propagation and query patterns.
11. Symptom: Hot partitions -> Root cause: Skewed partition key values -> Fix: Rebalance keys or shard aggregates.
12. Symptom: Cost runaway -> Root cause: Unbounded retention or high throughput to analytics -> Fix: Categorize events and tier retention.
13. Symptom: Stale read models -> Root cause: Consumer errors silently dropping updates -> Fix: Monitor projection lag and errors, add alerting.
14. Symptom: Security breach on events -> Root cause: No encryption or weak IAM -> Fix: Enforce mTLS and least-privilege roles.
15. Symptom: Frequent rollbacks -> Root cause: Tight coupling and transactional assumptions -> Fix: Rework services to be eventually consistent and add compensations.
16. Symptom: Observability noise -> Root cause: Too many low-value metrics -> Fix: Focus on SLIs and aggregate metrics, reduce cardinality.
17. Symptom: Schema sprawl -> Root cause: Uncoordinated event creation -> Fix: Maintain event catalog and governance.
18. Symptom: Incomplete postmortems -> Root cause: Lack of event traceability -> Fix: Include event catalogs and correlation ids in postmortems.
19. Symptom: Long saga durations -> Root cause: External dependencies blocking steps -> Fix: Add timeouts and async compensations.
20. Symptom: Consumer incompatibilities after upgrade -> Root cause: Breaking changes without versioning -> Fix: Use versioned schemas and feature flags.

Observability pitfalls included above: missing trace context, insufficient broker telemetry, noisy metrics, missing correlation ids, silent consumer errors.

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Best Practices & Operating Model

Ownership and on-call:

• Assign event ownership to producer team for schema and contract changes.
• Consumer teams own their processing SLIs and on-call rotation.
• Shared escalations for cross-cutting failures.

Runbooks vs playbooks:

• Runbooks: step-by-step recovery for known failures (DLQ processing, replay).
• Playbooks: higher-level decisions for ambiguous incidents (escalate, rollback plan).

Safe deployments:

• Canary deployments for producers and consumers.
• Schema compatibility checks in CI to prevent breaking changes.
• Feature flags and gradual rollout for event shape changes.

Toil reduction and automation:

• Automate DLQ triage with tooling for replay and selective reprocessing.
• Automate schema checks in CI and promote schema via registry APIs.
• Automate consumer autoscaling by lag-based policies.

Security basics:

• Encrypt events in transit and at rest.
• Use strong authentication and authorization between producers and brokers.
• Mask or separate PII fields at the event boundary; consider tokenization.

Weekly/monthly routines:

• Weekly: Review DLQ growth and top failing event types.
• Monthly: Audit schema registry changes and event ownership.
• Quarterly: Event catalog cleanup and retention policy review.

What to review in postmortems related to Event Storming:

• Which events were involved and their timeline.
• Which services owned the events and their response times.
• Whether runbooks and compensations were effective.
• Recommendations for schema or contract changes.

What to automate first:

• Schema compatibility checks in CI.
• Basic publish/consume metrics and trace propagation.
• DLQ monitoring and automated replay tooling.

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Tooling & Integration Map for Event Storming (TABLE REQUIRED)

ID	Category	What it does	Key integrations	Notes
I1	Schema Registry	Stores event schemas and versions	CI, brokers, producers	Enforce compatibility rules
I2	Message Broker	Routes and persists events	Producers, consumers, monitoring	Partitioning and retention policies
I3	Tracing	Visualizes end-to-end flow	Apps, brokers, logs	Requires async context propagation
I4	Metrics Platform	Aggregates SLIs and SLOs	Apps, dashboards, alerts	Tag metrics by event type
I5	Log Aggregator	Centralizes structured logs	Apps, DLQ inspection	Useful for postmortems
I6	Contract Testing	Validates producer/consumer contracts	CI pipelines	Prevents integration regressions
I7	Replay Tooling	Replays historical events safely	Storage, brokers	Must handle idempotency
I8	DLQ Manager	Monitors and routes failed events	Brokers, alerts	Provides triage and reprocessing UI
I9	Identity/IAM	Secures access to topics and APIs	Brokers, cloud IAM	Enforce least privilege
I10	Feature Flag	Controls event behavior migration	CI, prod	Useful for rolling out schema changes

Row Details (only if needed)

• None

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Frequently Asked Questions (FAQs)

H3: What is the first step in running an Event Storming workshop?

Start by defining scope, inviting domain experts and technical stakeholders, and selecting a facilitator and a collaboration medium.

H3: How do I pick which events to model first?

Prioritize events with the highest business impact, cross-team dependencies, or frequent failure modes.

H3: How long should an Event Storming session last?

Varies / depends; short sessions can be 2–4 hours for small scopes, full-day for complex domains, multi-day for enterprise-wide efforts.

H3: How do I ensure schema compatibility across teams?

Use a schema registry, enforce compatibility rules in CI, and adopt consumer-driven contract testing.

H3: How do I measure the success of Event Storming?

Measure downstream improvements: fewer incidents related to event flows, faster onboarding, clearer contracts, and SLO attainment on event SLIs.

H3: What’s the difference between Event Storming and Event-Driven Architecture?

Event Storming is a discovery process; Event-Driven Architecture is a design outcome that may be informed by Event Storming.

H3: What’s the difference between Event Storming and Domain-Driven Design?

DDD is a broader modeling discipline; Event Storming is a specific workshop technique often used within DDD.

H3: What’s the difference between Event Storming and Story Mapping?

Story Mapping prioritizes user journeys and features; Event Storming focuses on domain events and system behavior.

H3: How do I include SRE in Event Storming?

Invite SREs to map SLIs, discuss failure modes, and define runbooks for event-related incidents.

H3: How do I troubleshoot missing events in production?

Check broker health, DLQ, consumer logs, and traces correlated by event id; replay safely if needed.

H3: How do I handle PII in event payloads?

Design events to minimize PII in transit, tokenize or reference PII via IDs, and enforce encryption and access controls.

H3: How often should we revisit event models?

At every major feature, quarterly for active domains, or after relevant incidents.

H3: How do I get cross-team buy-in for Event Storming?

Highlight business impact, involve stakeholders early, and produce actionable artifacts like schemas and runbooks.

H3: How do I scale Event Storming in a large org?

Use trained facilitators, split by bounded contexts, and aggregate results into an event catalog and governance framework.

H3: How do I prevent event schema sprawl?

Enforce registration, ownership, versioning policies, and periodic cleanups of unused events.

H3: How do I test event flows?

Use contract tests, integration tests with test brokers, and replay tests on staging.

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Conclusion

Event Storming is a practical, collaborative technique that grounds architecture, product, and operations in the events that matter. It reduces ambiguity, informs telemetry and SLO design, and helps teams build resilient event-driven systems with clearer ownership and safer deployments.

Next 7 days plan:

• Day 1: Define scope and invite stakeholders; schedule workshop and prepare materials.
• Day 2: Run a focused Event Storming session for a single bounded context.
• Day 3: Extract top 10 events and create initial schemas; add basic publish/consume metrics.
• Day 4: Register schemas and add contract checks to CI pipeline.
• Day 5: Build on-call runbooks and dashboards for the mapped events.

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Appendix — Event Storming Keyword Cluster (SEO)

• Primary keywords
• Event Storming
• Domain events
• Event-driven design
• Event storming workshop
• Event mapping
• Domain-Driven Design events
• Event modeling
• Event storming facilitation
• Event storming techniques

• Event storming examples

• Related terminology

• Domain Event
• Command and Event
• Saga pattern
• Choreography vs Orchestration
• Event sourcing
• CQRS pattern
• Schema registry
• Message broker
• Dead-letter queue
• Idempotency key
• Correlation ID
• Trace context
• Observability for events
• SLIs for events
• SLOs for event delivery
• Event consumer
• Event producer
• Event catalog
• Event mesh
• Pub sub patterns
• Partition key strategy
• Sequence numbers
• Replay tooling
• Contract testing for events
• Consumer-driven contracts
• Event schema versioning
• Backpressure handling
• Consumer autoscaling
• Event-driven microservices
• Event-driven architecture patterns
• Read model projection
• Projection lag
• Compensating transactions
• Replay safety
• Event replay strategy
• Event registry governance
• Event-driven CI
• DLQ management
• Event telemetry
• Event audit trail
• Cross-service event tracing
• Event partitioning best practices
• Event retention policies
• Real-time analytics events
• Event-based feature flags
• Event enrichment
• Event-driven security
• PII in events
• Event-driven cost optimization
• Event-driven chaos testing
• Event-driven postmortem
• Event-driven runbooks
• Event-driven automation
• Event-driven rehearsal
• Event-driven data pipeline
• Event-driven observability dashboard
• Event-driven incident response
• Event-driven replay testing
• Event-driven schema evolution
• Event-driven contract enforcement
• Event-driven compliance audit
• Event-driven monitoring alerts
• Event-driven burn rate
• Event-driven canary deploy
• Event-driven rollback strategy
• Event-driven tooling map
• Event-driven managed services
• Event-driven Kubernetes patterns
• Event-driven serverless
• Event-driven managed pubsub
• Event-driven tracing propagation
• Event-driven sampling strategies
• Event-driven retention tiers
• Event-driven data governance
• Event-driven team ownership
• Event storming facilitator
• Event storming agenda
• Event storming artifacts
• Event storming outcomes
• Event storming anti-patterns
• Event storming glossary
• Event storming case studies
• Event storming playbook
• Event storming decision checklist
• Event storming maturity ladder
• Event storming for SRE
• Event storming for product teams
• Event storming for enterprise
• Event storming for startups
• Event storming remote workshop
• Event storming virtual board
• Event storming sticky notes
• Event storming mapping techniques
• Event storming discovery
• Event storming integration mapping
• Event storming telemetry planning
• Event storming runbook generation
• Event storming contract generation
• Event storming schema validation
• Event storming governance model
• Event storming ownership model
• Event storming onboarding
• Event storming cross-team alignment
• Event storming backlog prioritization
• Event storming cost performance tradeoffs
• Event storming observability pitfalls
• Event storming best practices
• Event storming metrics
• Event storming SLIs SLOs
• Event storming alerting strategy
• Event storming data flow
• Event storming lifecycle
• Event storming failure modes
• Event storming mitigations