Thousands of customers turn to Dynamic Tables for a modern and fast approach to data transformations. With end-to-end pipeline latency in minutes and an efficient incremental processing engine, Dynamic Tables provide a modern and scalable approach to autonomous pipelines. Over the past year, Snowflake has shipped a wave of updates that make Dynamic Tables faster, more expressive and more interoperable with the tools you already use.
At Summit, Sergey Labetsik, a senior data engineer at Wind Creek Hospitality, demonstrated how his team was able to deliver food vouchers to guests within a minute of eligibility. By migrating a dbt batch job to a Dynamic Tables pipeline, they cut end-to-end latency to under a minute, a vast improvement from the 30-minute schedule that the job had been running on.
Here's what's new with Dynamic Tables and why it matters for your pipelines.
Benchmarks of up to 2.8x faster refresh performance
Figure 1: Benchmarks showing up to 2.8x faster refresh performance on Dynamic Tables.Speed is the foundation on which everything else builds. We benchmarked the most popular Dynamic Table patterns from May 2025 to May 2026 and measured up to 2.8x faster refresh performance. This reflects updates we made under the hood to accelerate performance on Dynamic Tables, including top-level aggregate functions, QUALIFY row/rank = 1 (SCD-1), cluster-by operations and joins — all measured on Gen2 warehouses.
