Event-driven architecture (EDA) has become the default approach for many financial services applications, promising scalability, modularity, and real-time responsiveness. However, traditional choreography-based EDA often introduces complexity that undermines these benefits. Temporal offers a new paradigm for building resilient, maintainable systems by centralizing control through durable execution and workflow orchestration.
This blog post explores the limitations of traditional EDA, Temporal’s unique approach, and practical strategies for implementing it in financial services.
The Problem with Choreography-Based EDA
Choreography-based EDA structures applications around independent microservices that react to events autonomously. This design often seems like a natural fit for distributed systems, offering runtime flexibility and loose coupling between services. However, this approach frequently leads to substantial challenges, especially as systems scale in complexity.
- Scattered Business Logic. With choreography, the logic for even simple workflows is fragmented across multiple services. Each service maintains its own state, processes events independently, and communicates through loosely defined contracts. This scattering makes it nearly impossible to understand the complete workflow from a single vantage point. Debugging requires sifting through logs and tracing events across numerous services, increasing the time and effort required to resolve issues.
- Hidden Coupling. While choreography-based EDA promises loose coupling at runtime, the reality is more nuanced. Services become tightly coupled at the design level because they depend on shared event formats and protocols. A seemingly minor change — such as altering an event schema — can cascade through the system, breaking multiple services and leading to costly maintenance efforts.
- Error Handling Complexity. In a choreography-based system, there is no centralized mechanism to manage errors, retries, or compensations. Each service must implement its own logic to handle failures. For example, if a service crashes while processing a payment request, the system could lose track of whether the payment was completed, resulting in inconsistencies like double charges or missed transactions. These gaps often force teams to rely on manual intervention, which is both inefficient and error-prone.
- Limited Observability. Choreography distributes state across services, hiding it in local databases, queues, and event logs. This lack of visibility creates a black box effect, where developers must piece together incomplete information to diagnose problems or understand system behavior. The underlying issue is that choreography places events at the center of the system, making them the de facto unit of coordination. This creates incidental complexity — challenges that arise not from the problem itself but from the design choices made to solve it. Systems designed this way are brittle, hard to scale, and expensive to maintain.
Temporal: A Shift to Durable Execution
Temporal addresses these issues by replacing event-centric design with durable execution. This approach centralizes business logic in Workflows that are resilient, observable, and easy to manage.
Key Benefits
- Stateful Workflow Management: Temporal maintains the state of Workflows across outages, ensuring processes resume from the exact point of failure without manual intervention.
- Integrated Error Handling: Temporal provides built-in mechanisms for retries, compensations, and timeouts, making Workflows more reliable.
- Unified Observability: Temporal’s centralized view of Workflow execution allows developers to trace and debug processes efficiently. Instead of fragmenting logic across services, Workflows in Temporal encapsulate the entire business process, while features like Nexus enable better communication between services. This makes systems easier to reason about, modify, and scale.
Best Practices for Implementing EDA with Temporal
To fully leverage Temporal’s capabilities, consider these technical strategies:
- Workflows as Code. Temporal Workflows are written in standard programming languages like Go, Java, and Python. This enables version control, unit testing, and easier collaboration compared to DSLs or graphical tools like BPMN.
- Modular Activities. Break Workflows into modular Activities, each representing a specific unit of work (e.g., calling an API or writing to a database). This modularity simplifies testing and reuse.
- Error Recovery and Compensation. Define retry policies and compensation workflows to handle failures gracefully. For example, in a multi-step transaction, Temporal can roll back earlier steps if a downstream service fails.
- Dynamic Workflow Design. Leverage Temporal’s ability to dynamically define workflows based on external configurations or runtime conditions. This flexibility enables workflows to adapt to changing business requirements without major rewrites.
- Incremental Adoption. Integrate Temporal into specific components of your architecture before scaling its usage. For example, use Temporal to replace a state machine in one service, then expand its role over time.
Next Steps: Future-Proofing Your Architecture
Temporal not only addresses the pitfalls of choreography-based EDA but also enhances its core benefits, such as modularity and scalability. By centralizing orchestration, Temporal enables financial institutions to reduce risk, improve reliability, and accelerate modernization.
For a deeper dive into how Temporal can transform your event-driven systems, download our white paper on risk and reliability in financial services. To explore the technical nuances of EDA design, check out our comprehensive EDA guide. Both resources provide actionable insights for building resilient architectures in high-stakes environments.