A model is not a diagram for its own sake; it is a useful simplification that helps a team make decisions and write better software. You will practice choosing what to include, what to ignore, and how to test whether a model is useful.

Trace how Domain Driven Design grew from object-oriented programming, enterprise software pain, Eric Evans’s blue book, agile delivery, and later microservice practice. This history explains why DDD cares so much about language, boundaries, and collaboration.

Separate the big business problem into smaller areas of knowledge and responsibility. You will identify core, supporting, and generic subdomains so teams know where deep modeling matters most.

A bounded context gives a model a safe place where its words and rules mean one thing. You will practice drawing context boundaries around teams, workflows, data ownership, and language changes.

Context maps show how models relate when they cannot all be the same model. This chapter covers customer-supplier, conformist, shared kernel, partnership, anticorruption layer, and separate ways relationships show up in real systems.

Distillation helps teams protect attention for the parts of the domain that create the most value. You will practice deciding what deserves custom modeling, what can be bought, and what should stay simple.

Event storming turns a business process into a wall of events, commands, actors, policies, and questions. You will run a lightweight session that reveals hidden rules, handoffs, risks, and better boundaries.

Domain storytelling and example mapping use concrete stories to uncover rules and edge cases. You will turn user examples into shared facts that can guide design, testing, and conversations with experts.

Value objects represent descriptive facts such as money, dates, measurements, names, and addresses. You will design them so invalid states are hard to create and important rules live close to the data they protect.

Entities carry identity across time, even when their details change. This chapter covers identity, lifecycle, equality, state changes, and the mistakes that happen when every data row becomes an entity too early.

Aggregates group related objects around rules that must stay consistent together. You will choose aggregate roots, protect invariants, set transaction boundaries, and avoid large object graphs that slow change.

Some domain behavior does not naturally belong on a single entity or value object. This chapter shows when to use domain services, factories, repositories, and policies without turning the model into a collection of thin data containers.

Domain events record something meaningful that already happened in the business. You will name events in business language, decide what data they carry, and use them to reduce coupling between parts of a system.

A domain model needs protection from databases, web frameworks, message brokers, and UI concerns. This chapter covers layered architecture, hexagonal architecture, ports, adapters, and dependency direction in DDD systems.

Persistence should store domain decisions without taking over the design. You will work through repositories, object-relational mapping tradeoffs, transactions, migrations, and when a simpler data model is enough.

Many domains depend on time: deadlines, renewals, approvals, holds, cancellations, and delayed consequences. This chapter covers state machines, temporal rules, policy objects, scheduled actions, and audit needs.

Validation is more than checking form fields; it includes invariants, permissions, workflow rules, and cross-context decisions. You will place each kind of rule where it can be enforced clearly and tested reliably.

Tests can document the language and rules of the domain. This chapter covers behavior-focused unit tests, specification by example, acceptance tests, test data builders, and avoiding tests that only mirror implementation details.

Follow one feature from domain question to delivered code: clarify language, find boundaries, model rules, design aggregates, write tests, persist state, publish events, and review the result with domain experts. This is the first complete DDD workflow in the course.

When bounded contexts need to talk, the contract matters as much as the code. You will design APIs, commands, queries, published language, versioning rules, and anticorruption layers that protect each model.

Asynchronous messaging lets systems react without forcing every decision into one transaction. This chapter covers eventual consistency, idempotency, retries, ordering, outbox patterns, message schemas, and failure handling.

CQRS separates the model that changes state from the model that answers questions. You will decide when separate read and write models help, how projections are built, and when CQRS adds needless complexity.

Event sourcing stores a stream of facts instead of only the latest state. This chapter covers event streams, rebuilding state, snapshots, schema evolution, audit trails, and the kinds of domains where event sourcing is worth the cost.

Many business processes cross aggregates, services, teams, and time. You will design sagas and process managers that coordinate long-running work while keeping local models clean and failures recoverable.

Microservices work best when service boundaries follow real domain boundaries, not technical layers. This chapter connects bounded contexts to service ownership, data ownership, deployment independence, and the dangers of distributed monoliths.

A modular monolith can give strong boundaries without the cost of a distributed system. You will structure modules around domains, enforce dependencies, evolve toward services only when needed, and keep a codebase changeable over time.

DDD boundaries are also team boundaries. This chapter covers stream-aligned teams, platform teams, enabling teams, cognitive load, handoffs, and how Team Topologies changed the way many organizations apply DDD.

Older systems often contain valuable domain knowledge buried in tables, screens, reports, and workarounds. You will use seams, strangler patterns, context mapping, and discovery techniques to replace or surround legacy code safely.

Cloud-native systems add concerns that affect domain design: scaling, resilience, managed services, deployment frequency, and operational ownership. This chapter shows how to keep domain boundaries clear while using containers, serverless workflows, queues, and managed databases.

Good DDD systems make business behavior visible in production. You will connect domain events, logs, metrics, traces, dashboards, and alerts to the language of the business so teams can diagnose real workflow problems.

Security, privacy, legal duties, and approval rules are often part of the domain, not afterthoughts. This chapter shows how to model authorization, consent, audit trails, retention, fraud controls, and compliance workflows without hiding them in generic infrastructure.

Modern AI tools can speed up note cleanup, example generation, code sketches, and documentation, but they can also invent false domain rules. You will use AI assistants safely for DDD work by grounding prompts in real examples, reviewing outputs with experts, and protecting private information.

DDD work produces many artifacts: context maps, event storming boards, C4 diagrams, ADRs, API contracts, test examples, and living documentation. This chapter shows which artifacts to keep, how to update them, and how to avoid documentation that drifts away from practice.

Pull together the full practice: discovery, strategic design, tactical modeling, implementation, integration, testing, release, observation, and feedback. You will run a realistic DDD project loop and decide what to change when new domain knowledge appears.

Close the course with the paths people take into DDD work: developer, architect, analyst, product partner, tech lead, consultant, and facilitator. You will plan proof-of-skill artifacts, useful reading, community practice, conference talks, and ways to keep current as DDD evolves.