📌 Event-Driven Architecture Summary
Event-Driven Architecture (EDA) is a software design pattern where systems communicate by producing and responding to events. Instead of following a strict sequence, different parts of the system react whenever something happens, such as a user action or a change in data. This approach allows systems to be more flexible, scalable and easier to update, as new features can be added by simply listening to new events without changing the entire system.
🙋🏻♂️ Explain Event-Driven Architecture Simply
Imagine a group chat where anyone can send a message and others can choose to reply or ignore it. Each person reacts when they receive a message that interests them, instead of waiting for their turn. In event-driven architecture, different parts of a system pay attention to specific events and act only when something relevant happens.
📅 How Can it be used?
You could use event-driven architecture to build a notification system that sends alerts whenever users receive new messages or updates.
🗺️ Real World Examples
Online retailers use event-driven architecture to update inventory and notify customers. When an item is purchased, an event is triggered, updating available stock and sending confirmation emails without slowing down the checkout process.
Banks often use event-driven architecture for fraud detection. Each transaction triggers an event that can be analysed in real time, allowing the system to quickly react and alert staff or customers if suspicious activity is detected.
✅ FAQ
📚 Categories
🔗 External Reference Links
Event-Driven Architecture link
Ready to Transform, and Optimise?
At EfficiencyAI, we don’t just understand technology — we understand how it impacts real business operations. Our consultants have delivered global transformation programmes, run strategic workshops, and helped organisations improve processes, automate workflows, and drive measurable results.
Whether you're exploring AI, automation, or data strategy, we bring the experience to guide you from challenge to solution.
Let’s talk about what’s next for your organisation.
💡Other Useful Knowledge Cards
Token Governance Frameworks
A token governance framework is a set of rules and processes that help a group of people make decisions about how a digital token system is run. These frameworks outline how token holders can suggest changes, vote on proposals, and manage shared resources or policies. The goal is to ensure fairness, transparency, and efficient decision-making in projects that use tokens for coordination.
Log Analysis Pipelines
Log analysis pipelines are systems designed to collect, process and interpret log data from software, servers or devices. They help organisations understand what is happening within their systems by organising raw logs into meaningful information. These pipelines often automate the process of filtering, searching and analysing logs to quickly identify issues or trends.
Ticketing System Automation
Ticketing system automation refers to the use of software tools to handle repetitive tasks in managing customer support tickets. This can include automatically assigning tickets to the right team members, sending updates to customers, or closing tickets that have been resolved. The goal is to speed up response times, reduce manual work, and make support processes more efficient.
Campaign Management System
A Campaign Management System is a software platform that helps organisations plan, execute and track marketing or advertising campaigns. It centralises the process of creating messages, scheduling delivery, managing budgets and monitoring results. This system often includes tools for targeting specific audiences, automating repetitive tasks and generating performance reports.
Epoch Reduction
Epoch reduction is a technique used in machine learning and artificial intelligence where the number of times a model passes through the entire training dataset, called epochs, is decreased. This approach is often used to speed up the training process or to prevent the model from overfitting, which can happen if the model learns the training data too well and fails to generalise. By reducing the number of epochs, training takes less time and may lead to better generalisation on new data.