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This article examines how ETL frameworks and Kafka Streams can be utilized to enhance data management in corporate information systems. It discusses integrating data from various sources into a coherent system for improved decision-making, employing technologies like Apache Spark for data processing and predictive analytics, and leveraging Kafka Streams for real-time data integration and analysis. The article provides high-level architecture examples and code snippets to illustrate the concepts, emphasizing the significance of scalable and fault-tolerant systems in efficiently managing large data volumes and facilitating business growth through data-driven insights.

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