Data Warehousing

A typical data platform architecture

Warehouse performance problems are almost always schema and query pattern problems, not hardware limitations, and throwing more compute at a poorly clustered table just makes the bill bigger without fixing the underlying issue. We design schemas around your actual query patterns, not a theoretically pure normalized model, since analytical workloads reward denormalization and clustering choices that transactional systems would never use. Whether the target platform is Snowflake, BigQuery, or Redshift, we tune around the specific cost model of that platform, Snowflake's per-second compute billing rewards different patterns than BigQuery's per-byte-scanned pricing, and a warehouse optimized for one doesn't automatically translate to the other. Every warehouse we build includes query performance monitoring and a documented approach to partitioning and clustering, so performance degradation as data volume grows gets caught early rather than discovered when a dashboard that used to load in seconds starts timing out. We also right-size compute allocation explicitly, since over-provisioned warehouses are one of the most common and easiest to fix sources of runaway cloud data spend.