Concepts

The ideas behind Event Sourcing, CQRS, and Domain-Driven Design, explained without any product, along the five stages of the lifecycle.

Modeling
What is Domain-Driven Design?

Domain-driven design (DDD) is an approach to software design that puts the business domain at the center of the system.

Event-Storming

This guide introduces Event-Storming as a collaborative modeling technique for discovering and designing events in event-sourced systems.

Modeling Events

This guide explains how to model events effectively in an event-sourced system.

Designing Aggregates

This guide explains how to design aggregates in event-sourced systems.

Dynamic Consistency Boundaries (DCBs)

Event-sourced systems often use aggregates to define where consistency must be preserved.

Remembering
Introduction to Event Sourcing

This guide introduces the concept of event sourcing and explains how it differs from traditional approaches to storing application state.

What is CQRS?

CQRS stands for Command Query Responsibility Segregation.

Use Cases

This guide helps you understand where event sourcing can be most effectively applied.

Designing Read Models

This guide explores how to design effective, purpose-driven read models in an event-sourced system.

Read-Model Consistency and Lag

This guide explains how to manage the consistency and freshness of read models in event-sourced systems.

Eventual Consistency in Practice

This guide explains what eventual consistency means in the context of event-sourced systems and how to design applications that behave correctly even when data is temporarily out of sync.

Snapshots and Performance

This guide explains how to use snapshots in event-sourced systems to improve performance and reduce overhead during replay.

Optimizing Event Replays

This guide explains how to optimize the performance and structure of event replays in event-sourced systems.

Patterns for Temporal Queries

This guide explores patterns for performing time-based queries in event-sourced systems.

Versioning Events

This guide explains how to evolve event types and structures in a safe, predictable, and maintainable way.

GDPR Compliance

This guide explains how to assess and achieve GDPR compliance in event-sourced systems.

Common Mistakes

This guide describes common mistakes and misunderstandings that arise when working with the event sourcing model.

Building
Deciders

This guide explains the Decider pattern.

Hexagonal Architecture

This guide explains Hexagonal Architecture, also known as Ports and Adapters.

Building Event Handlers

This guide explains how to build event handlers in event-sourced systems.

Building Event-Driven Applications

This guide explains how to design and implement event-driven applications on top of an event store.

Task-Based UIs

This guide explains task-based user interfaces.

Testing Event-Sourced Systems

This guide explains how to test event-sourced systems.

Building with AI

This guide explains why event-sourced systems are particularly well suited to being built with the help of AI coding agents, and how to set up the work so that an agent produces reliable code rather than plausible guesses.

Integrating
What is Event-Driven Architecture?

This guide explains Event-Driven Architecture (EDA).

Domain Events and Integration Events

This guide explains the difference between the events a system records for itself and the events it publishes to others.

Reliable Delivery

This guide explains how to make sure that events reliably leave the system that produced them and reach every system that depends on them.

CloudEvents

This guide explains what CloudEvents are, why they matter for event-sourced systems, and how the specification helps standardize event data across systems.

Data Mesh and Data Products

This guide explains Data Mesh, an approach to sharing data across an organization, and the data products it is built on.

Analyzing
Event Sourcing and AI

This guide explains why an event-sourced history is the best raw material for analytics, machine learning, and AI, and how the path from events to insights is structured.

Designing Analytical Projections

This guide explains how to turn an event stream into datasets for analysis, statistics, and machine learning.

Point-in-Time Correctness

This guide explains how to prevent data leakage when datasets for analytics or machine learning are built from an event history.

Features from Events

This guide explains how to derive features – the measurable variables a model learns from – from an event history.

LLMs and Event Streams

This guide explains how large language models (LLMs) can work directly with an event history – answering questions in plain language, exploring patterns, and helping to investigate what happened.

Causal Inference from Event Streams

This guide explains how an event history helps to tell cause and effect apart from mere correlation.

Closing the Loop

This guide explains how predictions become actions, and how the results of those actions flow back into the event history to make the next predictions better.