How does data partitioning affect storage and compute costs in Snowflake?

Data partitioning in Snowflake primarily reduces compute costs by enabling the query engine to prune irrelevant micro‑partitions, thereby scanning less data and consuming fewer compute credits. Because fewer bytes are read, the amount of compute time required for a query decreases, which directly translates to lower compute charges. Storage costs are affected in two ways: first, micro‑partitioning improves compression, which can lower the amount of storage needed; second, the metadata overhead of maintaining many micro‑partitions can slightly increase storage usage. In practice, the savings from reduced compute often outweigh the modest increase in storage. Additionally, partitioning can improve concurrency by isolating workloads, which can reduce contention and further lower the cost of running multiple queries simultaneously. Overall, effective partitioning leads to a more cost‑efficient Snowflake deployment by balancing storage savings with compute reductions.