Event-driven Architectures and Microservices

Back-end & Platform Engineering Solution

context​

In today’s software development landscape, companies face the increasing need for systems that are not only scalable and flexible but also capable of reacting in real-time to events and changes. Traditional monolithic architectures, with their rigid and interconnected structure, often prove inadequate to meet these demands, becoming a bottleneck for innovation and growth. 

In this scenario, microservices architectures, particularly when implemented using the event-driven paradigm, emerge as an effective solution to address these challenges.

healthcare

PAIN POINTs

  • Maintenance difficulties: any change can require interventions across the entire system.
  • Limited scalability: it is only possible to scale the entire application, not individual modules.
  • Complex deployments: even small changes require the release of the entire monolith.
  • Low team autonomy: teams have to constantly coordinate, reducing efficiency.
  • Complicated testing: it is difficult to isolate components and test independently.
  • High coupling: modules are highly interdependent, making the system fragile.
  • Reduced reliability: an error in one component can compromise the entire system.
  • Slowed time to market: the release cycle is slower, hindering rapid innovation.

solution

Our expertise focuses on implementing microservices architectures based on modern and scalable solutions that implement consolidated architectural patterns and utilize cutting-edge technologies. This approach is based on building systems composed of autonomous services that communicate primarily through events.

The core of Bitrock’s proposal is an evolutionary architecture that integrates several complementary approaches to maximize flexibility, scalability, and responsiveness:

  • Event-Driven Architecture: A service publishes an event on an event bus (such as Apache Kafka), and other interested services subscribe to that type of event and process it asynchronously. This decoupling makes services more independent and resilient.
  • Microservices: Each microservice is an autonomous unit of business functionality, which can be developed, deployed, and scaled independently from others. Microservices focus on specific tasks, promoting modularity and maintainability.
  • CQRS (Command Query Responsibility Segregation): This pattern separates read operations (queries) from write operations (commands). In an event-driven architecture, events are used to update read models, ensuring consistency and optimal performance for queries.
  • Loose Coupling Principles: We minimize dependencies between services to increase flexibility and resilience. Event-driven design contributes significantly to loose coupling

 

Real-time data processing is crucial in event-driven architectures. We use stream processing platforms like Apache Kafka to manage event streams, enabling data analysis, transformation, and enrichment.

Our solution stands out for its ability to integrate legacy systems with modern architectures, allowing a gradual and safe migration towards a more agile and high-performance infrastructure. Particular attention is paid to the design of fault-tolerant systems that ensure operational continuity even in scenarios of high load or in the presence of partial failures.

benefits

  • Service decoupling: microservices communicate through events, reducing direct dependencies between them.
  • Increased scalability: each service can be scaled independently, based on its own load.
  • Resilience: an error in one service does not necessarily impact others; the architecture is more fault-tolerant.
  • Asynchronicity: services do not have to wait for immediate responses, improving overall performance.
  • Faster time to market: autonomous teams can develop and release services independently.
  • Technological flexibility: each microservice can use the most suitable language and technology for its purpose.
  • Traceability and auditing: with a persistent event log (e.g., Kafka), it is possible to trace and replay events precisely.
  • Extensibility: it is easy to add new event consumers without modifying producers.
  • Adaptability to change: the architecture better supports business evolution and integration with new systems.
Technology Stack and Key Skills​

 

  • Apache Kafka
  • Kafka Streams
  • Flink
  • KSQL
  • CDC (Change Data Capture)
  • Relational Database
  • NoSQL databases
  • Stream Processing
  • Event-driven Architecture
  • CQRS Pattern
  • Microservices Design
  • Distributed Systems

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