A logistics provider's Snowflake spend was growing faster than its data volume or user count, and finance wanted an explanation before approving the next commitment. AceMQ analyzed consumption against the work each warehouse was actually performing.
Most of the growth came from structural choices rather than genuine demand. Warehouses had been sized up to fix individual slow queries and never sized back down. Auto-suspend was set high enough that warehouses idled for long stretches while still billing. Several teams shared warehouses, so contention drove further size increases that benefited nobody in particular.
Snowflake on AWS serving business intelligence, dbt transformations, and ad-hoc analysis across multiple teams.
AceMQ built consumption attribution down to warehouse, workload, and query pattern so decisions could be made from evidence instead of averages. Sizing was then set per workload against its measured profile, with auto-suspend tuned to the trade-off between idle billing and cache warmth rather than to a default.
Credit consumption dropped meaningfully without degrading query performance for any workload, and the growth curve flattened against data volume. Finance now has attribution by team, so cost conversations reference specific workloads rather than a single aggregate number.
Assessment of clustering keys, micro-partition pruning, and table design on large tables where queries had begun scanning most of the data.
Resolving pipeline runtime blowouts caused by queries spilling to remote storage on undersized warehouses while concurrent jobs queued behind them.
Whether you need architecture advisory, 24/7 support, or full managed services, AceMQ has the expertise to help.