Architecting Real-time Predictive Analytics Pipelines Using Snowflake and AWS Lambda for IoT Data Streams

Abstract

As the Internet of Things (IoT) proliferates, vast volumes of streaming sensor data are being generated in real-time, creating both opportunities and challenges for data-driven decision-making. This paper proposes a cloud-native architecture that integrates Snowflake’s scalable data platform with AWS Lambda's serverless compute capabilities to build real-time predictive analytics pipelines for IoT data. By leveraging AWS services for ingestion (such as Kinesis or MQTT over IoT Core), Lambda for event-driven processing, and Snowflake for scalable storage and analysis, the proposed solution enables rapid deployment of machine learning models to process streaming data. The architecture is designed to be low-latency, cost-effective, and easily extensible for a variety of industrial applications. A prototype implementation and performance evaluation are presented, demonstrating the effectiveness of the architecture in handling high-throughput, low-latency IoT workloads.