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Event-driven architecture

Summarize this topic in 1 paragraph. Put a section heading for each paragraph. You MUST put double square brackets around each computer buzzword, product name, or jargon or technical words. Answer in MediaWiki syntax.

Event-driven architecture is a software design pattern where the flow of information and processing within a system is driven by the occurrence of events. In this architecture, components of the system communicate asynchronously through the production, detection, and consumption of events. Events represent significant occurrences or state changes within the system, and they are typically published to event channels or queues, enabling other components to react to them in real-time or near-real-time. Event-driven architecture promotes loose coupling between system components, allowing for greater flexibility, scalability, and resilience. This pattern is commonly used in distributed systems, microservices architectures, and systems where responsiveness to changes is critical.

Event-driven architecture

Summarize this topic in 15 paragraphs. Put a section heading for each paragraph. You MUST put double square brackets around each computer buzzword, product name, or jargon or technical words. Answer in MediaWiki syntax.

```mediawiki

Event-driven architecture (EDA) is a software design paradigm that emphasizes the production, detection, consumption, and reaction to events. An event is any significant change in state that can be identified and processed by components within a system. This architecture enables highly responsive and scalable applications, as components communicate through asynchronous events rather than direct calls. EDA is particularly well-suited for environments where data input is continuous and varied, such as real-time analytics, IoT devices, and complex event processing systems.

The core components of EDA include event producers, event consumers, and the event bus. Event producers are sources of events, which can be anything from user interactions to sensor outputs. Event consumers react to the events, processing or responding as necessary. The event bus acts as the intermediary, facilitating the transfer of events from producers to consumers without requiring them to be directly connected, enhancing modularity and flexibility.

EDA offers several benefits, including improved scalability, flexibility, and responsiveness. By decoupling producers and consumers, systems can easily scale up or down as needed. This architecture also allows for greater flexibility in updating or extending applications, as new event consumers can be added without impacting existing functionality. Additionally, the asynchronous nature of event processing enables systems to efficiently handle high volumes of events, leading to faster response times.

Despite its advantages, EDA also presents certain challenges. The asynchronous and distributed nature of event-driven systems can complicate debugging and monitoring, making it harder to trace the path of an event through the system. Ensuring event consistency and managing event flow can also be difficult, especially in systems with a high volume of events or those that require events to be processed in a specific order.

EDA is particularly effective in scenarios where systems need to react to real-time data streams or complex event patterns. Use cases include real-time analytics, fraud detection, IoT device monitoring, and dynamic user interfaces. By leveraging EDA, organizations can build applications that are more responsive to user actions or environmental changes, enhancing user experience and operational efficiency.

Event sources in an EDA are diverse, ranging from user actions, such as clicks and swipes, to system metrics like CPU usage or temperature readings. External triggers, such as webhooks from other applications or services, can also generate events. The variety of sources underscores the flexibility of EDA, enabling its application across different domains and scenarios.

Event processing involves filtering, aggregating, or transforming events as they move from producers to consumers. This can be handled by specialized components in the architecture, such as event processors or stream processing engines. These components can perform complex analyses, like pattern matching or anomaly detection, enabling sophisticated responses to event streams.

EDA supports various communication patterns, including publish/subscribe and event streaming. In publish/subscribe, event producers broadcast events without knowing the consumers, who subscribe to types of events they're interested in. Event streaming involves continuous flow of data, where events are stored in a log, allowing consumers to process past and present events, enhancing the system's ability to handle time-series data.

The technology stack for implementing EDA includes messaging systems like Apache Kafka and RabbitMQ, event buses like AWS EventBridge, and serverless computing services like AWS Lambda and Azure Functions. These technologies provide the infrastructure for event generation, transmission, and processing, forming the backbone of event-driven systems.

Designing an EDA involves careful consideration of event granularity, system boundaries, and event schemas. Event granularity refers to the level of detail included in an event, which impacts system performance and usability. Defining system boundaries helps in determining the scope of events and how they are propagated. Event schemas ensure consistency and understandability of events across the system.

EDA can be integrated with other architectural styles, such as microservices architecture, to leverage the benefits of both approaches. In such integrations, each microservice can act as an event producer and/or consumer, enabling highly scalable and flexible systems. This hybrid approach combines the modularity of microservices with the responsiveness of EDA.

Security in EDA involves ensuring the integrity and confidentiality of events as they are transmitted across the system. This includes implementing authentication and authorization for event access, encrypting sensitive data within events, and monitoring event traffic for potential security threats. Proper security measures are crucial to protect the system from malicious actors.

The future of EDA is closely tied to advancements in cloud computing, IoT, and real-time analytics. As these technologies continue to evolve, the adoption of EDA is expected to increase, particularly in applications requiring high levels of scalability and responsiveness. Additionally, developments in AI and machine learning

will enhance the capability of event-driven systems to analyze and respond to complex event patterns.

Best practices for EDA include defining clear event contracts, employing effective event versioning strategies, and monitoring event-driven workflows. Establishing clear contracts ensures consistency and reduces integration issues. Versioning allows for smooth transitions and compatibility between different versions of event schemas. Monitoring is essential for maintaining system health and performance.

Event-driven architecture represents a powerful paradigm for building responsive, scalable, and flexible applications. Despite its challenges, the benefits of decoupling components, enhancing scalability, and enabling real-time responsiveness make EDA a compelling choice for a wide range of applications. As technology continues to evolve, EDA will likely play an increasingly important role in software architecture. ```

Event-driven architecture Alternatives - Alternatives to Event-driven architecture

Summarize the alternatives to Event-driven architecture in 5 paragraphs. Put a section heading for each paragraph. You MUST put double square brackets around each computer buzzword, product name, or jargon or technical words. Answer in MediaWiki syntax.

```mediawiki

Monolithic architecture represents a traditional model of software design where applications are built as a single and indivisible unit. This approach allows for straightforward development and deployment processes since all components of the application – including the database operations, client-side logic, and server-side logic – reside within a single codebase. However, monolithic architectures can become increasingly complex and cumbersome as the application grows, making it difficult to scale, update, or maintain individual components without affecting the entire system.

Service-oriented Architecture (SOA) is a design approach where applications are composed of discrete and loosely coupled services. Each service is designed to fulfill a specific business function and communicates with other services through well-defined interfaces and protocols, such as SOAP or REST. SOA facilitates flexibility and reusability, allowing organizations to modify and redeploy individual services without impacting the rest of the system. This architecture supports integration and interoperability among disparate systems, making it suitable for complex, enterprise-level applications.

Microservices architecture is a method of developing software applications as a collection of small, autonomous services, each running in its own process and communicating with lightweight mechanisms, often an HTTP-based API. Microservices allow for decentralized data management and are designed around business capabilities, enabling agile development and deployment practices. This architecture enhances scalability and allows individual components of the application to be updated or scaled independently, providing a flexible alternative to more traditional monolithic designs.

Serverless architecture is a cloud-computing execution model where cloud providers dynamically manage the allocation and provisioning of servers. Applications are built using third-party services (Backend as a Service, BaaS) or custom code that's run in ephemeral containers (Function as a Service, FaaS), with scaling handled automatically by the cloud service provider. This approach reduces the need for traditional server management and capacity planning, allowing developers to focus on writing code and building features rather than managing infrastructure.

Hybrid architectures combine elements of cloud computing and on-premises infrastructure to leverage the advantages of both environments. This approach can include any mix of monolithic architecture, SOA, microservices architecture, and serverless architecture, providing the flexibility to choose the best solution for specific components of an application. Hybrid architectures offer the security and control of on-premises solutions while still providing the scalability and resource efficiency of cloud services, making it an adaptable option for businesses with complex or evolving needs. ```

Event-driven architecture

List the competing alternatives in 1. AWS Event-driven architecture, 2. Azure Event-driven architecture, 3. GCP Event-driven architecture, 4. IBM Event-driven architecture, 6. IBM z Mainframe Event-driven architecture, 7. Oracle Event-driven architecture, 8. Kubernetes Event-driven architecture, 9. VMWare Event-driven architecture / Tanzu Event-driven architecture, 10. Alibaba Event-driven architecture, 11. DigitalOcean Event-driven architecture, 12. Huawei Event-driven architecture, 13. Tencent Event-driven architecture, 14. On-Premises Data Center Event-driven architecture using Open Source Cloud / Private Cloud Technologies. When you make a numbered list, precede the number with an asterisk and a space. Put a section heading for each paragraph. You MUST put double square brackets around ALL computer buzzwords, product names, or jargon or technical words. Answer in MediaWiki syntax.

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Amazon Web Services (AWS) offers a comprehensive event-driven architecture with services like AWS Lambda for serverless compute, Amazon SNS for notification services, and Amazon SQS for queue management. These services work together to allow applications to respond to changes in real time, enabling scalable and efficient applications.

Microsoft Azure provides an event-driven architecture through Azure Functions, which support serverless computing, and Azure Event Grid, a fully managed event routing service. Azure Service Bus and Azure Queue Storage offer additional messaging capabilities, facilitating complex communications and workflows.

Google Cloud Platform (GCP) embraces event-driven architecture with Google Cloud Functions, allowing for the execution of code in response to events. Google Pub/Sub provides messaging and ingestion for event-driven systems, and Google Cloud Workflows automates sequences of API calls.

IBM offers an event-driven architecture through IBM Cloud Functions, based on Apache OpenWhisk, providing serverless computing capabilities. IBM Event Streams is an event-streaming platform built on Apache Kafka, enabling high-throughput, resilient message streaming.

The IBM z Mainframe supports event-driven architecture by integrating traditional mainframe processing with modern event-driven applications. This is achieved through middleware and tools that facilitate the consumption of events by mainframe applications, bridging the gap between old and new technologies.

Oracle Cloud implements event-driven architecture using Oracle Functions, which is their serverless computing service, and Oracle Cloud Infrastructure Events for event routing and response. This combination allows for the development of highly responsive, scalable applications.

Kubernetes supports event-driven architecture through extensions and third-party tools like Knative, which offers components for building serverless applications and event-driven systems on top of Kubernetes, providing scalability and management of event-driven workloads.

VMWare Tanzu supports event-driven architecture within the Kubernetes ecosystem, providing tools and services for building and managing event-driven applications and services. This includes the Tanzu Application Service and Tanzu Kubernetes Grid for orchestrating and managing containers.

Alibaba Cloud's event-driven architecture features Alibaba Cloud Function Compute for serverless computing and Alibaba Cloud EventBridge for event bus services. This allows for the building of scalable and flexible applications that respond to real-time events.

DigitalOcean offers a simpler approach to event-driven architecture with its managed services like DigitalOcean Functions for serverless computing, allowing developers to run code in response to events without managing the underlying infrastructure.

Huawei Cloud supports event-driven architecture through Huawei Cloud FunctionGraph, a serverless computing service, and Huawei Cloud Distributed Message Service for messaging and event streaming, enabling efficient communication and processing of events.

Tencent Cloud embraces event-driven architecture with Tencent Cloud SCF (Serverless Cloud Function) for running serverless applications and Tencent Cloud EventBridge for event management, allowing for scalable and responsive application development.

For on-premises data centers, event-driven architecture can be implemented using open source cloud technologies such as Apache Kafka for event streaming, OpenFaaS or Kubeless for serverless computing, and NATS for messaging. These technologies enable the creation of event-driven systems in a private or hybrid cloud environment, providing flexibility and control over infrastructure. ```

Event-driven architecture Best Practices

Summarize this topic in 20 paragraphs. Put a section heading for each paragraph. You MUST put double square brackets around ALL computer buzzwords, product names, or jargon or technical words. Answer in MediaWiki syntax.

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A foundational best practice in event-driven architecture (EDA) is to establish clear and consistent event models. This involves defining the structure, format, and semantics of events. Properly modeled events ensure that event producers and event consumers can communicate effectively, facilitating easier integration and scalability. Utilizing standard JSON or XML formats for event payloads can enhance interoperability among different components and systems.

Decoupling components is a core principle of EDA. By ensuring that event producers and event consumers are loosely coupled, systems become more resilient and adaptable to change. This practice enables components to operate independently, reducing dependencies and allowing for easier updates and maintenance. Employing an event bus or message broker like Apache Kafka or RabbitMQ can facilitate this decoupling by acting as an intermediary layer.

Event sourcing is a strategy where changes to the application state are stored as a sequence of events. This approach provides several benefits for EDA, including improved auditability, traceability, and the ability to reconstruct past states. Event sourcing can complement EDA by providing a robust mechanism for state management across distributed systems.

Incorporating Domain-Driven Design (DDD) principles can enhance the effectiveness of EDA by aligning event models with the business domain. This ensures that events are meaningful and relevant to the business processes they represent. DDD helps in identifying bounded contexts and defining domain events that trigger business processes, leading to a more intuitive and maintainable system design.

Event partitioning is a technique to distribute events across multiple channels or partitions to enhance scalability and performance. This is particularly important

Event-driven architecture Glossary:

Give 10 related glossary terms with definitions. Each topic on a separate line followed by a second carriage return. You MUST put double square brackets around each computer buzzword or jargon or technical words. Answer in MediaWiki syntax.

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• Event: A significant change in state or an update that occurs within a system, which can be identified, produced, and consumed by components within an event-driven architecture.

• Event Producer: A component or service that generates events, often based on actions such as user interactions, system changes, or external triggers.

• Event Consumer: A component or service within an event-driven architecture that listens for and processes events. It reacts to the events it is interested in, which were published by event producers.

• Event Bus: A communication channel that decouples event producers from event consumers, allowing events to be published and subscribed to in a scalable and flexible manner.

• Message Broker: Middleware that facilitates message exchange between applications by translating messages between formal messaging protocols. It plays a crucial role in ensuring that messages are delivered to the correct event consumers in an event-driven architecture.

• Publish-Subscribe Pattern: A messaging pattern where messages (events) are published by producers without the knowledge of who will consume them. Subscribers express interest in one or more classes of message, and only receive messages that are of interest, without knowing about the producers.

• Event Stream: A sequence of events generated over time that can be processed, analyzed, or stored. Event streams are used in event-driven architectures for continuous data processing and real-time analytics.

• Event Sourcing: A design pattern where changes to the application state are stored as a sequence of events. This allows the system to reconstruct past states and provides an audit log of all changes made over time.

• Event Driven Middleware: Software infrastructure that supports the creation, detection, consumption, and reaction to events. This includes event buses, message brokers, and complex event processing engines.

• Complex Event Processing (CEP): A method of processing event data in real-time to identify meaningful patterns, relationships, and conditions among multiple streams of event data. CEP is used to detect complex patterns and situations across various events.

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```mediawiki Event Storming: A workshop-based method used in Domain-Driven Design (DDD) to quickly find and explore domain events, commands, and aggregates by bringing together domain experts and development teams.

Eventual Consistency: A consistency model used in distributed computing where updates to a distributed system are propagated asynchronously, and the system is guaranteed to become consistent at some future point, assuming no new updates are made.

Message Queue: A form of asynchronous service-to-service communication used in serverless and microservices architectures, where messages are stored on a queue until they can be processed by a consumer.

Command Query Responsibility Segregation (CQRS): A design pattern where the system is divided into two parts: one that handles commands (actions that change state) and another that handles queries (actions that return state), to improve performance, scalability, and security.

Saga Pattern: A pattern used to manage long-running business transactions and ensure data consistency across microservices by coordinating a sequence of local transactions.

Dead-letter Queue: A special queue used to store messages that could not be processed successfully, allowing developers to inspect and possibly reprocess them.

Fan-out Pattern: A messaging pattern where a message published to a topic is copied and forwarded to all subscribers, allowing for the broadcasting of messages to multiple recipients.

Reactive Programming: A programming paradigm oriented around data flows and the propagation of change, which makes it easier to develop dynamic models where changes propagate automatically across the system.

Webhooks: User-defined HTTP callbacks that are triggered by specific events in a web application, allowing external services to be notified when certain events occur.

Service Mesh: A dedicated infrastructure layer for facilitating service-to-service communications between services or microservices, providing features like load balancing, service discovery, encryption, and traceability. ```

```mediawiki

• Event Storming: An interactive, group-based exercise used to explore complex business domains and design event-driven systems. It helps teams identify domain events, commands, and aggregates, facilitating a deeper understanding of business processes and requirements.

• Saga Pattern: A design pattern used to manage business transactions that span multiple microservices by sequencing event emissions and corresponding local transactions. It ensures data consistency and integrity across distributed services without requiring distributed transactions.

• Event Carrying Data Transfer: A pattern where events carry the state data required by the consumer to process the event, minimizing dependencies and decoupling between services by reducing the need to query the state of other services.

• Event Sourcing: A persistence mechanism that stores state transitions as a series of events, allowing systems to reconstruct current state by replaying past events. This approach provides audit trails and enables temporal querying of state.

• Command Query Responsibility Segregation (CQRS): A design pattern that separates the read and write operations of a data store into distinct interfaces. This approach can improve performance, scalability, and security, and is often used in conjunction with event sourcing.

• Materialized View: A read model of data that aggregates and projects information from various events or services. It provides a queryable view of the current state, optimized for specific use cases or queries, often used in CQRS systems.

• Domain-Driven Design (DDD): A software development approach that emphasizes the complexity of building applications by connecting the implementation to an evolving model of the core business concepts. It involves identifying bounded contexts, aggregates, entities, and value objects.

• Event Deduplication: A technique used to ensure that each unique event is processed only once by a consumer, even if it is delivered multiple times. This is crucial in maintaining idempotency and consistency in systems where duplicate messages might occur.

• Backpressure: A feedback mechanism used in systems to control the flow of data between services, preventing overwhelm and ensuring stability. It allows consumers to signal producers to slow down when they are unable to process incoming events at the current rate.

• Event Schema: A formal definition of the structure and format of events, including the data types, fields, and relationships between fields. Schemas ensure consistency in event production and consumption, and can be versioned to manage changes over time.

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Snippet from Wikipedia: Event-driven architecture

Event-driven architecture (EDA) is a software architecture paradigm concerning the production and detection of events.

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