Event sourcing is a data modeling technique that captures changes in a system as a sequence of discrete events, rather than only storing the current state. It’s not new, but it’s becoming more common as modern systems increasingly operate in an event-driven way.

This article outlines the fundamentals of event sourcing, use cases, key implementation challenges, and practical ways to integrate it without overengineering your data architecture.

Suppose you work in data engineering, analytics, or architecture. This is a concept worth understanding, particularly if your work depends on history, traceability, or a clear view of how processes unfold.

What is event sourcing?

Event sourcing means storing every meaningful change as a discrete event rather than just the object’s current state.

You don’t just store the current status of an order. Instead, you store the full sequence of actions that led to it: “Item added to cart” → “Payment completed” → “Order shipped”.

Each event is:

• A historical fact
  
• Bound to a specific time
  
• Immutable (you never change it once written)
  

By replaying the events, you can reconstruct the current state, or any state at any point in time.

Think Accounting

A helpful analogy: event sourcing is like double-entry bookkeeping.

In accounting, you don’t just say: “We currently have €5,000 in the bank.”
You record each journal entry that got you there: a sale, a payment, a tax adjustment. The ledger shows how you got to today’s balance.

Event sourcing works the same way: events are the journal entries of your data model. The end state is useful. But the path to it? That’s where the real insight lives.

Or, if accounting doesn’t click for you:

Imagine a video game that records every move you make.

You can watch the replay, pause, rewind, and understand how you won or lost.

Event sourcing works the same way: your data system keeps every move (event), not just the final score.

What are the benefits of event sourcing?

1. Unifying differences across source systems

In a typical data warehouse, data flows in from multiple source systems, each with its own structure, naming conventions, and change recording. Some systems are naturally event-driven. Others are not.

• Often event-driven: web tracking, mobile apps, logging pipelines, e-commerce platforms, modern microservices
  
• Often state-based: CRM systems, ERPs, finance software, HR tools
  

By applying event sourcing at the warehouse level, you create a uniform model for representing change: everything is expressed as a time-stamped event. This gives you a consistent foundation, regardless of how the source system works.

It simplifies integration, improves comparability, and reduces the need for custom logic per system in your downstream models.

2. Robustness through immutability

In these event-sourced models, events are immutable: once written, they are never updated or deleted. This principle brings clarity and confidence: what happened is stored as it happened, without silent corrections or hidden overrides.

This improves traceability and auditability, and simplifies your data processing pipelines. Your transformation logic becomes more predictable and stable over time, because the data doesn’t change retrospectively. You don’t have to constantly deal with late-arriving changes or retroactive corrections. Instead, you process new events as they come in.

In large-scale data systems, that stability is gold. It reduces operational load, the risk of inconsistent outputs, and the need for backfilled jobs or logic workarounds. Traditional data pipelines often struggle with late-arriving updates or corrections. In an immutable event model, you simply add new events instead of rewriting history.

3. Future-readiness – Aligning with an event-driven world

Many modern systems, such as web applications, mobile platforms, and microservices, are inherently event-driven. They emit events natively: user actions, system triggers, API calls.

By adopting an event-based model in your data warehouse, you don’t have to retrofit changes from status tables or build fragile logic to guess what changed. Instead, your data structure mirrors the behavior of the source system.

This alignment makes your architecture easier to maintain, especially as systems evolve or get replaced. You’re not tied to a specific version of a CRM or ERP; you’re working with raw, time-stamped facts that are resilient to schema shifts or process redesigns.

When should I use event sourcing?

Event sourcing works best when:

• You care about history, context, and sequence
  
• You need traceability (e.g. audits, legal compliance)
  
• You want to understand user behaviour, process flow, or conversion paths
  
• Your sources are already event-driven (or will be)
  

That said, it doesn’t have to be all or nothing. You can combine event logs with slowly changing dimensions (SCD) or status tables where appropriate.

A practical example of event-sourcing

In one of our recent projects, we applied event sourcing even though most of the source systems were traditional state-based platforms, not inherently event-driven.

Still, it made sense. Why?

• The data consumers (analysts, stakeholders) were primarily interested in what happened, not just the end state
  
• There was a strong need for auditability and transparency, both internally and externally
  
• And we wanted to build a future-proof foundation that could adapt as source systems evolve
  

By designing around a central event log, we created a clear, consistent model of process steps even though the original systems didn’t explicitly record events. The result was easier to work with, more traceable, and better aligned with future reporting needs. It also reduced reliance on how individual systems store data, giving analysts one reliable view across processes.

What does event sourcing bring to AI and data agents?

Modern AI, from predictive models to generative agents, depends on context.
Large Language Models (LLMs) and data agents need clean data and an understanding of how and why that data changed.

When your data model stores only the latest state, you lose the sequence of events that explains behavior. Techniques like Slowly Changing Dimensions (SCDs) or snapshots can capture parts of that history: they tell you what changed and when.
But they don’t always show how things changed or in what order events occurred.

Event sourcing preserves that full sequence.
It captures each meaningful moment as it happens, giving AI systems and data analysts a narrative to reason over. A complete timeline of decisions, interactions, and outcomes. That richer context helps models and agents understand not just the state of things, but the story behind them.

This is what makes it indispensable in the age of AI:

• Conversational and agentic AI systems can answer “how did we get here?” and “what happens next?” using real event trails, not static aggregates.
  
• Generative AI can use event histories as grounding data, reducing hallucinations and producing contextually accurate responses that reflect real-world cause and effect.
  
• Predictive models also benefit from event-sourced data. They’re usually trained on aggregated features rather than raw events, but those features become more accurate and flexible when they’re derived from a complete, time-stamped history.
  
• AI observability improves, since event logs make it easier to trace model inputs, decisions, and feedback loops over time.
  

Event sourcing does more than ensure traceability; it gives structure to time and context.
It gives your data a living memory, one that AI can learn from, interact with, and grow through.

What are the common challenges when adopting event sourcing?

For many teams, event sourcing takes some getting used to. Not because the concept is overly complex, but because it challenges familiar habits. These are some of the common questions or hesitations we hear from clients, along with the ways we’ve learned to navigate them.

❌ It’s a different way of thinking

You model “what happened” instead of “what it is.” That takes a mental shift for developers and analysts.

✅ Solution:

Don’t overcomplicate it. Treat events like journal entries in accounting: each one captures a fact in time. Use naming conventions such as _event_log, show flows in simple diagrams, and offer example queries. People get it faster than you think, especially if the structure is consistent across systems.
It’s not a problem. It’s just a new standard.

❌ Event volumes grow fast

If you store everything, your tables will grow quickly. Especially in high-frequency domains like clickstream data.

✅ Solution:

• Use cloud-native tools with scalable storage (e.g. Snowflake, BigQuery, Databricks)
  
• Introduce a layered model: raw event logs below, status views above. Analysts query the simplified layer, not the raw logs.
  

❌ Reconstructing the current state is an effort

Want to know the latest status of something? You’ll need to process all events up to that point.

✅ Solution:

• Build materialized status tables that are periodically updated
  
  

❌ Not all systems provide events

Some systems (like CRMs or ERPs) only expose current status, not changes over time.

✅ Solution:
Use snapshot techniques (e.g. dbt snapshots or Change Data Capture) to detect changes and convert them into synthetic events. It’s not as elegant, but it gets you close to a true event log.

Most of these aren’t real blockers but rather just practical considerations that needed a clearer approach.

Choose change over state

Event sourcing gives you more than technical control; it gives you strategic clarity and confidence.

You move from static snapshots to a living record of how things change over time.
That shift transforms your data warehouse from a fragile system of facts into a resilient system of clarity.

When you understand not just what happened, but how and why, you reach a new level of adaptability. More reliable data. Clearer insights. And a foundation that evolves with your business. 

🎯 Want to convert status tables into events?
🎯 Not sure when to use SCD vs events?
🎯 Curious if your tooling supports this model?

We’re happy to have a chat.