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

  • Authors

    • Ritu Agarwal Finance Manager, Capgemini, India. Author

    DOI:

    https://doi.org/10.67228/30715717/IJDEIC-2018PII7Y4D

    Published 11-08-2018

  • Real-Time Analytics, Iot Data Streams, AWS Lambda, Snowflake, Serverless Architecture, Predictive Analytics, Edge Computing, Data Pipeline, Streaming Data, Cloud Computing

    Issue

    Vol. 1 No. 2 (2018)

    Section

    Articles

    How to Cite

    [1]
    R. Agarwal, “Architecting Real-time Predictive Analytics Pipelines Using Snowflake and AWS Lambda for IoT Data Streams”, IJDEIC, vol. 1, no. 2, pp. 01–08, Nov. 2018, doi: 10.67228/30715717/IJDEIC-2018PII7Y4D.

  • 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.

  • References

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