Title Event-driven data orchestration: A modular approach for high-volume real-time processing

2025-05-16//1 min read

🔗Abstract

This article presents a model for orchestrating data extraction, processing, and storage, addressing the challenges posed by diverse data sources and increasing data volumes. The proposed model includes three primary components: data production, data transfer, and data consumption and storage. Key architectures for data production are explored, such as modular designs and distributed processes, each with advantages and limitations regarding scalability, fault tolerance, and resource efficiency. A buffering module is introduced to enable temporary data storage, ensuring resilience and asynchronous processing. The data consumption module focuses on transforming and storing data in data warehouses while providing options for parallel and unified processing architectures to enhance efficiency. Additionally, a notification module demonstrates real-time alerts based on specific data events, integrating seamlessly with messaging platforms like Telegram. The model is designed to ensure adaptability, scalability, and robustness for modern data-driven applications, making it a versatile solution for effective data flow management.

Keywords: big data, producer, consumer, buffer, event-driven