Abstract:

The rapid growth of enterprise data has intensified the demand for automated, scalable, and cost-efficient pipelines that seamlessly integrate data engineering and machine learning. Snowflake Cortex introduces a unified, serverless framework that enables organisations to build end-to-end ETL and ML workflows directly within the Snowflake Data Cloud, eliminating the operational overhead and fragmentation typically associated with external ML platforms. This paper examines the design, implementation, and performance of automated ETL and ML workflows powered by Cortex Functions, Snowpark, and native orchestration features. We demonstrate how Cortex accelerates feature engineering, simplifies model development, and supports real-time and batch inference—all while maintaining strict governance, security, and data locality. Through practical case studies—including fraud detection, churn prediction, and demand forecasting—we evaluate the operational efficiency, latency improvements, and cost optimisations achieved by consolidating the data-to-ML lifecycle inside Snowflake. Our results show that Cortex-based automation reduces pipeline complexity by up to 50%, improves time-to-production for ML models, and offers a more reliable path to enterprise-scale AI adoption. This study provides both a technical framework and empirical evidence for organisations seeking to modernise their data and ML operations using Snowflake Cortex.

Keywords:

Automated ETL Pipelines, Snowflake Cortex, Serverless Machine Learning, MLOps Automation, Unified Data and AI Platforms

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