Your First Event Pipeline
Define what an event actually *is* — fact vs. command vs. message — and walk a single event end-to-end through a producer, broker, and consumer before any schema decisions are on the table.
Event-Driven Design
Event schema design, naming standards, evolution contracts, and governance patterns.
Streaming systems live or die on event design. Schemas that evolve safely, names every team understands, and contracts the CI enforces — that's the difference between a platform that scales to 100 services and one that breaks every Friday deploy.
What you’ll be able to do
- Design event schemas with required fields, idempotency keys, and partition decisions that survive production
- Implement schema evolution with backward and forward compatibility enforced in CI
- Build producer-side validation pipelines with DLQs, replay, and the outbox pattern
- Establish event architecture governance with catalogs, PII tagging, and ownership
Curriculum
Phase 1: Event Fundamentals
First pipeline and event basics
Event Fundamentals
Anatomy of a production-safe event: required fields (event_id, occurred_at, event_type, version), idempotency keys, ordering guarantees, and the partitioning decisions that lock in for the life of the topic.
Phase 2: Schema & Standards
Event schema design, naming, and evolution
Event Schema Design
JSON Schema and Avro structural rules that catch nulls, enums, and type drift at the producer — including the additionalProperties: false decision and how to model nested entities without producing fragile schemas.
Naming & Modeling Standards
Naming conventions ({domain}.{entity}.{action}), past-tense verb rules, key vs. value placement, and the modeling tradeoffs (fat events vs. thin events, embedded vs. referenced entities) that decide replayability.
Schema Evolution & Contracts
Backward / forward / full compatibility modes in Confluent Schema Registry, breaking-change taxonomy, contract tests in CI, and a versioning strategy (v1/v2 topics vs. in-place evolution) that keeps consumers alive.
Phase 3: Reliability & Governance
Validation, architecture patterns, and governance
Event Validation & Reliability
Validate at the producer, not the consumer: schema-registry enforcement, dead-letter queues, poison-pill handling, exactly-once boundaries, and the dual-write problem solved with the outbox pattern.
Event Architecture Patterns
Choose between event notification, event-carried state transfer, event sourcing, and CQRS based on consumer count, query patterns, and audit needs — and avoid event-sourcing the simple service.
Event Architecture Governance
Build the event catalog (Confluent Stream Catalog / Backstage / DataHub) on top of the schema registry, with PII tagging, ownership, change approval workflows, and discoverability across teams.
What you’ll build
- A versioned event-schema repo with backward-compatibility CI gates and a v1→v2 migration playbook
- A producer-side validation pipeline that rejects malformed events at the source — with DLQ + replay tooling
- A naming + modeling style guide every team consumes, with linter rules and a worked order.placed reference event
- An event catalog wired to Schema Registry, surfacing PII tags, owners, downstream consumers, and SLA per topic
Without this skill, your event platform breaks every time anyone changes anything.
WHAT GOES WRONG
- Schema-change cascades — Shopify-style: one producer adds an optional field, four downstream consumers break overnight because nobody enforced compatibility
- Phantom data gaps — events flow but lack event_id; consumer dedup logic silently drops them and a 2-hour analytics gap shows up at the Monday review
- Null-revenue incidents — schema registered as nullable, producer ships a refactor, finance discovers 12 hours of null order_total on a Friday night
- Invisible-to-governance topics — 14 schemas with PII never registered in the catalog because catalog registration was self-service and optional
What is Event-Driven Design?
Event-driven design is the practice of defining how systems communicate through events — including schema structure, naming conventions, evolution contracts, and governance. Well-designed events are the foundation of reliable streaming systems at companies like Uber, Shopify, and Netflix, where thousands of services exchange millions of events per second.
Why this matters in production
Poorly designed events cause cascading failures across microservices. At Shopify, event schema evolution without proper contracts broke hundreds of downstream consumers. Production event design requires backward compatibility, validation, and governance that prevents breaking changes from reaching production.
Common use cases
- Designing event schemas with proper structure, naming, and versioning
- Implementing schema evolution with backward and forward compatibility
- Building event validation pipelines that catch malformed events before processing
- Establishing naming conventions and modeling standards across engineering teams
- Creating event catalogs and documentation for cross-team discovery
- Governing event architecture with approval workflows and contract enforcement
EVENTDRI vs alternatives
EVENTDRI vs API Design
Event design handles asynchronous communication between decoupled systems. API design handles synchronous request-response patterns. Events enable loose coupling; APIs enable tight integration.
EVENTDRI vs Data Contracts
Event design focuses on event structure and evolution. Data contracts are broader, covering any data interface between teams. Event schemas are a specific type of data contract.
EVENTDRI vs Schema Registry
Event design defines the principles and patterns for events. Schema registry (like Confluent Schema Registry) is the tool that enforces schema compatibility. Design comes first; registry enforces it.
Related skills
- Kafka & Stream Processing — Well-designed events are transported through Kafka, covered in
- Data Observability — Event validation and contracts pair with pipeline observability in
- Streaming Fundamentals — Event design principles apply to all streaming systems in
Why this skill matters
This is the skill that separates streaming engineers who ship from streaming engineers who page on-call. Staff and senior streaming engineers at Uber, Shopify, Netflix, and Confluent are paid for exactly this — making event-driven systems evolvable instead of brittle.
Common questions about EVENTDRI
What is event-driven design?
Event-driven design defines how systems communicate through events — schema structure, naming, versioning, and governance. It ensures events are reliable, compatible, and discoverable across teams.
Why does event schema design matter?
Bad event schemas cause cascading failures when they change. Good schemas enable independent evolution of producers and consumers, preventing breaking changes in distributed systems.
How long does it take to learn event design?
Fundamentals take 1-2 weeks. Implementing schema evolution, validation, and governance patterns across a production system typically takes 4-6 weeks of practice.
What is schema evolution?
Schema evolution is safely changing event structure over time without breaking consumers. Backward-compatible changes add optional fields. Breaking changes require versioning and migration strategies.
Do data engineers need event design skills?
Yes, especially for teams building streaming systems. Event design skills prevent the integration failures that are the most common source of production incidents in event-driven architectures.
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