Real-Time IoT Sensor Data Streaming and Processing with Apache Flink: A Scalable Solution for Smart Monitoring
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Abstract
The Internet of Things (IoT) has revolutionized data-driven decision-making by enabling real-time data acquisition through an extensive network of sensors. However, the massive influx of continuous, high-velocity sensor data poses significant challenges for traditional data. The rapid explosion of sensor data, necessitating robust, scalable real-time data processing solutions. This paper presents a comprehensive solution for ingestion, processing, and analysis of large-scale sensor data streams in real time, based on Apache Kafka and Apache Flink. The framework handles continuous data flows with low latency and high throughput by leveraging Kafka's distributed streaming capabilities and Flink's advanced data processing features. This system is especially well-suited for applications that require immediate feedback, such as environmental monitoring, industrial automation, and smart city infrastructures. We provide a detailed case study that demonstrates the framework's efficiency in handling and processing high-velocity sensor data, emphasizing its potential to significantly improve decision-making processes in dynamic, data-driven environments. The findings highlight the framework's scalability and resilience, presenting a solid solution for organizations looking to use real-time analytics in their operations.
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