Episode notes
How Uber slashed the cost of highly selective "export" queries — like Data Subject Access Requests that pull a handful of records out of massive historical datasets. Jordan and Riley get into why these queries trigger full-partition scans that keep cold data "hot" and defeat GCS auto-tiering, why Uber passed on Hudi's Secondary Index, and the combo that actually worked: Hudi column stats for file pruning plus sorting tables by the predicate column so a user's records cluster into a few files. Reported ~24.8% disk reduction on a tested partition. Takeaway: access patterns and file layout drive storage cost more than raw data volume. Source: Running Cost-Efficient Export Workloads at Uber — Uber Engineering, Aug 12 2026 — https://www.uber.com/us/en/blog/running-cost-efficient-export/ This is commentary/summary in the hosts' own words, not a reproduction of the article.