What is Event-Driven Architecture?

An event-driven architecture (EDA) is a modern software design approach centered around data that describes events—selection of a button on a user interface (UI), the addition of an item to an online shopping cart, notification of payment on a point of sale (POS) system, etc.—in real-time and enables applications to act on them as they occur. This contrasts traditional request/response architectures that largely deal with data at defined intervals and respond to user-initiated requests rather than new information.

Event-driven architectures create, detect, consume, and react to events via four key components: publishers, subscribers, sources, and sinks. A publisher captures event data and stores it in a repository from which a subscriber consumes and potentially responds to it. Sources are where event data originates, and sinks are destinations where subscribers send it.

Event-driven architectures are versatile enough to handle unpredictable, nonlinear events and are particularly well suited for loosely coupled software such as microservices.

Real-Time Responsiveness

By leveraging event-driven patterns, applications can detect and respond instantly to events triggered by users and other systems with ultra-low latency. This allows for seamless and immediate communication, resulting in a more interactive and engaging user experience.

Scalability and Flexibility

For chat and messaging applications that handle many concurrent users and messages, scalability is critical. Event-driven architectures enable them to scale horizontally, distributing the workload across multiple instances or microservices. This ensures they can seamlessly accommodate increasing traffic and adapt to fluctuating demands without sacrificing performance or reliability.

Loose Coupling and Modularity

Event-driven architectures promote loose coupling, allowing application components to communicate without needing to know the specifics of one another’s implementation and ensuring that changes to one do not impact the others. These decoupled services can then be reused in different parts of a system or even in different applications altogether, reducing development time and effort and operational costs.

Fault Tolerance and Resilience

If one component of an event-driven architecture fails or becomes unresponsive, others can continue to function independently. This avoids a cascade of failures throughout a system and ensures it remains operational, improving overall reliability and availability.

Event Sourcing and Auditing

Event-driven architectures capture each change or action within a system as an event, providing a complete and chronological record and making tracking and auditing application behavior easier. Developers can use the event log for monitoring, debugging, and compliance.

Extensibility and Integration

By leveraging events, developers can easily and seamlessly integrate with third-party application programming interfaces (APIs), services, and databases and expand the functionality and extensibility of an application. This allows for the implementation of additional features and capabilities, such as user authentication, data storage, and analytics.

Security and Privacy

Event-driven architectures provide a secure and robust foundation for real-time chat and messaging applications by enabling developers to implement measures such as access control, authentication, and encryption at various levels. This ensures that unauthorized access is prevented and sensitive information is protected.

Asynchronous Processing

Rather than sending requests and waiting for responses before proceeding (synchronous communication), event-driven architectures process events independently. This allows applications to handle many of them simultaneously, improving overall performance and responsiveness.

Analytics

As they generate and process events in real-time, event-driven architectures collect rich data that can be analyzed to gain insights into user behavior, system performance, and more. Businesses can then use these to optimize applications and improve customer experiences through data-driven decisions.

Reduced Resource Consumption

Because event-driven architectures are push-based, systems handle events on demand as they happen—there is no need for continuous polling. This reduces central processing unit (CPU) utilization and lowers bandwidth consumption.

The event-driven design approach has become extremely popular in recent years thanks to both the explosion in sources that generate events and the development and adoption of technologies that process event streams. It enables businesses to think of their operations and the data they produce as a series of ongoing events rather than a handful of metrics on a weekly or quarterly report.

Event-driven applications often treat data as immutable, making it easy to look up values at points in time. Whenever information changes, they create a new data point with a new time period.

For example, consider the fictional characters Tara and Tina, who lived separately in numerous locations but have been roommates since 2013. If a user performed an address lookup for them in a traditional batch-based application, it would display their address as it is currently recorded. With an event-driven application, however, they could find out what Tara’s last address was before she and Tina moved in together, what their address was in 2014, and where they actually live now.

Event-driven architectures are particularly popular for microservices-based software applications. Microservices perform very specific tasks, and these tasks are often based on the occurrence of particular events.

Since microservices are typically components of a larger solution, there needs to be a way to coordinate them to run as a cohesive system. Within an event-driven architecture, each one is both a publisher and a subscriber to the data store. When it completes its work on the data, it writes an event data point in a topic or queue for consumption. This trigger kicks off the execution of the next microservice in the workflow.

Increased Complexity

With so many components and events involved, event-driven architectures can seem overwhelmingly complex. And their decoupled, nonlinear nature presents unique monitoring and debugging challenges.

A Steep Learning Curve

Challenges handling timing, sequencing, etc., due to asynchronous processing can make development, testing, and error handling difficult for developers who are often more familiar with traditional request/response architectures.

Compromised Security

With multiple publishers and subscribers shifting data points on and off the data store, event-driven architectures can be susceptible to unauthorized access, message tampering, and replay and denial-of-service (DoS) attacks.

Fraud Detection

Businesses can use event-driven architectures to run complex operations such as sophisticated, machine learning (ML)-based fraud algorithms as part of a payment processing workflow to reduce loss along with other operational costs.

Internet of Things (IoT)

With an event-driven architecture, the event data each IoT device captures can be consumed and responded to by subscribers nearly instantaneously. No matter where in the world the device is, they can tap into the event stream and get real-time information.

Payment Processing

Payment processing often entails a series of computations to determine the validity of a payment request and the optimal routing of a transaction across payment partners. This workflow is an ideal candidate for an event-driven architecture, as each phase is a contained, well-defined task that aligns with a microservices pattern.

Real-Time Marketing

Presenting the right deal at the right time boosts customer engagement—the challenge is identifying the best offer once the right time is recognized. An event-driven architecture allows businesses to seize these opportunities in real-time by gathering pertinent customer information during interactions and swiftly providing tailored recommendations.

Website Monitoring

Since event-driven architecture captures and processes events such as user interactions, system notifications, and external triggers in real-time, it is well suited to website traffic monitoring. It provides analytics that allows for faster decision-making and ensures that businesses can respond quickly.

Process Event Data Quickly and Gain Instant Insights

Event-driven architectures allow applications to process events in parallel without requiring custom push code for each subscriber. Real-time analysis immediately identifies trends and anomalies, providing the opportunity to evolve and grow.

Share Event Data Across Application Ecosystems

An event-driven architecture facilitates communication across decoupled systems. The data store establishes interoperability among components to exchange information while remaining autonomous.

Gain Efficiency and Scalability

Event-driven architectures overcome common challenges and reduce complexity via a standardized approach to service and application integration. They also optimize system performance by seamlessly adapting to fluctuating traffic demands.

Enhance Resilience and Accountability

A single failure can cause a complete breakdown. Event-driven architectures shield systems from this type of catastrophic event. They also provide traceability, which reduces errors and ensures data consistency and alignment.

Hazelcast works with data-driven businesses who turn to us for speed at scale, reliability, and security. Hazelcast Platform is a unified real-time data platform that uniquely combines a distributed, fast data store with a high-speed stream processing engine to run the fastest applications in any type of data-intensive environment—including one utilizing an event-driven architecture.

Develop and Deploy Applications With Ease

Hazelcast Platform was designed to simplify the software development life cycle (SDLC) by providing a familiar API that abstracts away the complexity of running a distributed application across multiple nodes in a cluster. This allows developers to spend more time on business logic and less on writing custom integration and orchestration code. Our platform can seamlessly integrate with your IT architecture to add new capabilities without you having to rip and replace your existing stack, and its cloud-native architecture requires no special coding expertise to gain the elasticity to scale up or down and meet highly fluctuating workload demands.

Optimize Workload Performance at Scale

Whether you process a large volume of transactions, enhance online experiences with faster responsiveness, or run large-scale transformations on data, Hazelcast Platform delivers the ultra-performance your workloads require. This proven performance advantage is especially valuable for data-focused experimentation that enables ongoing business optimization, such as data science initiatives like ML inference for fraud detection.

Maximize Reliability and Operational Effectiveness of Mission-Critical Systems

With built-in redundancy to protect against node failures and efficient WAN (Wide Area Network) Replication to support disaster recovery strategies that safeguard against total site failures, Hazelcast Platform provides the resilience required to run mission-critical systems. And its extensive integrated security framework protects data from unauthorized viewers while its security APIs allow custom controls for sensitive environments.