Citation :

Vishwanadham Mandala, 2024. "Exploring the Potential of Snowflake Analytics for Real-time Predictive Analytics" ESP International Journal of Advancements in Computational Technology (ESP-IJACT)  Volume 2, Issue 1: 90-100.

Abstract :

This paper discusses novel predictive modeling features of a cloud-based enterprise data warehousing platform (Snowflake) that allow for high-speed, nearly real-time updating of predictive models with new training data. The ability to process collaborative filtering-type recommendations is shown in the context of new Internet of Things (IoT) sensor data. The prediction update process is linear in the number of predictions and 68x +- 12x faster than batch updating methods if a linear model can be used. Raster data from 2D-simulated IoT sensors are also used to train a deep learning strategy that works directly with natively formatted S3 files in addition to Snowflake tables.

Under the operating assumption that platforms, languages, environments, and hardware will march through time, the current work tries to focus more on the development of a generally applicable big predictive data modeling methodology. Snowflake Analytics will inherently be included in this work as it tries to be generally applicable. The current work stays focused on exploring a new real-time estimator (in addition to a real-time loss) that is designed for Snowflake Inc.'s massively parallel processing data platform.

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Keywords :

Exploring the Potential of Snowflake Analytics, Industry 4.0, Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), Smart Manufacturing (SM), Computer Science, Data Science, Vehicle, Vehicle Reliability.