What are the benefits of data partitioning for query performance, especially in Snowflake's micro-partitioning model?

Data partitioning in Snowflake, often referred to as micro‑partitioning, is a key feature that improves query performance and storage efficiency. Each micro‑partition is a contiguous, columnar block of data that Snowflake automatically creates when data is loaded. Because predicates in a query can be evaluated against the metadata of these micro‑partitions, the engine can prune entire blocks that do not satisfy the filter, dramatically reducing the amount of data that must be read. This pruning leads to lower I/O, faster query times, and reduced compute credits. Additionally, grouping similar values together within a micro‑partition improves compression ratios, which in turn lowers storage costs. Partitioning also helps with concurrency by isolating workloads to specific blocks, reducing contention. However, over‑partitioning can create many small micro‑partitions, increasing metadata overhead and potentially offsetting performance gains. In summary, the main benefits of data partitioning in Snowflake are faster query performance through pruning, better compression and lower storage costs, and improved concurrency.