Architecting for Real-Time Decision-Making: Building Scalable Event-Driven Systems
Abstract
In the contemporary digital era, where data inundation is the norm and business landscapes are in constant flux, the imperative for real-time decision-making has never been more pronounced. Traditional batch processing paradigms, once stalwarts of data handling, now struggle to meet the demands of instantaneous insights and responses required by modern applications. This paper embarks on an exploration of the intricate architecture and design principles underpinning the construction of scalable event-driven systems tailored explicitly for real-time decision-making scenarios. Central to this discourse are the foundational pillars of event sourcing, stream processing, and microservices architecture, each playing a pivotal role in the orchestration of systems capable of seamlessly integrating and processing streams of events in real time. Event sourcing, with its focus on capturing and storing domain events as the sole source of truth, provides a robust mechanism for reconstructing system state and enabling temporal queries essential for real-time decision-making. Complementing this, stream processing frameworks empower organizations to ingest, process, and analyze vast volumes of streaming data with low latency, facilitating timely insights and responses to evolving situations. Furthermore, the adoption of microservices architecture facilitates the decomposition of complex systems into smaller, manageable services, each with its own bounded context and independent scalability. This modular approach not only enhances agility and fault isolation but also enables the seamless scaling of individual components to accommodate fluctuating workloads, thereby ensuring system responsiveness and reliability under varying conditions.However, the journey toward architecting real-time decision-making
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References
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