Data & AI By Mits Engineering Team • • 1 min read
A retail client was spending a small fortune reprocessing the same data every night. The pipeline worked - it was just brute-force expensive.
We started by profiling where the money actually went: full-table scans that only needed yesterday's deltas. Switching to incremental processing and columnar storage alone cut the bill by a third.
The rest came from an ML model that predicts which datasets are worth pre-aggregating and which can be computed lazily on demand. The result: a 62% cost reduction with faster dashboards, and an architecture the client's own team can operate without us.
