Union Logic

Design Considerations for Event Driven Architecture

Designing an effective event-driven architecture (EDA) requires careful consideration of various factors to ensure reliability, scalability, and performance. We closely work with clients to design and implement tailor-made EDA systems that align with their unique business objectives.

Event Types and Sources:

We identify the types of events your system will handle, such as user actions, data updates, external triggers, and system events.
We Determine the sources that generate these events and ensure proper integration with your architecture.

Event Schema and Payload:

We define a standardized event schema that includes essential metadata and payload information.
We choose a format for event payloads, such as JSON, Avro, or Protocol Buffers, to ensure compatibility and efficient serialization.

Consumer Groups:

We plan how event consumers will be organized into consumer groups to achieve load balancing, fault tolerance, and parallel processing.

Event Routing and Queuing:

We determine how events will be routed from sources to consumers.
We select appropriate message brokers or event streaming platforms (e.g., Apache Kafka, RabbitMQ) for reliable event queuing and distribution.

Event Processing Patterns:

We decide on the processing patterns to be used, such as publish-subscribe, point-to-point, or event sourcing.
We choose the most suitable pattern for different parts of your architecture based on requirements.

Scalability and Performance:

We design for horizontal scalability to accommodate increasing event volumes.
We select technologies that support partitioning, sharding, and load balancing for efficient processing.

Error Handling and Retry Mechanisms:

We implement error handling and retry mechanisms for handling failed events or processing errors.
We define policies for handling event delivery failures and ensuring eventual consistency.

Data Persistence:

We determine how event data will be stored for future analysis, auditing, or replay purposes.
We choose appropriate databases (e.g., relational, NoSQL) to store events and related data.

Event Ordering and Timestamps:

We address event ordering concerns by incorporating timestamp information and considering event causality.
We use mechanisms like event versioning to manage changes to event structures.

Idempotency:

We ensure that event processing is idempotent, meaning processing the same event multiple times produces the same outcome.
We design systems to handle duplicate events without unintended consequences.

Monitoring and Observability:

We plan for comprehensive monitoring and observability through logging, metrics, and tracing.
We Choose monitoring tools that allow you to gain insights into event flows, latencies, and system health.

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