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Abstract
Businesses today need real-time analytics, but traditional on-premises data warehouses—built for batch processing—often struggle to keep up. In this study, we compare Snowflake’s cloud-native streaming (powered by Snowpipe and dynamic scaling) with on-premises systems like Oracle and SQL Server, focusing on latency-sensitive use cases. Through controlled experiments simulating high-speed data streams (such as IoT sensors and financial transactions), we evaluate query latency, throughput, and resource efficiency across different workloads. Our early findings show that Snowflake dramatically cuts latency for real-time processing compared to batch-optimized on-premises solutions—though at higher costs during peak demand. Interestingly, we also pinpoint scenarios where on-premises systems still outperform Snowflake, particularly in predictable, large-scale batch operations. This research offers practical guidance for companies transitioning from legacy batch systems to cloud-based real-time analytics, helping them choose the right architecture for their needs.
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