Kuaishou's Data Lake Journey with Apache Hudi: Architecture Evolution, Use Cases, and Lessons Learned

The article presents Kuaishou's practical experience of building and operating a data lake using Apache Hudi, focusing on how the technology addresses data‑warehouse scale explosion, cross‑department collaboration inefficiencies, and real‑time versus offline data inconsistencies.

It outlines the three main business challenges: (1) continuous growth of data‑warehouse size leading to rising storage and compute costs and governance complexity; (2) low collaboration efficiency caused by mismatched timelines and difficult data change tracking across departments; (3) gaps between real‑time and offline data that affect timely decision‑making.

The technical evolution is described, moving from a Hive‑centric stack to Hudi because of three evaluation criteria—feature richness, compatibility with Kuaishou's existing big‑data ecosystem, and automation level. Hudi was chosen for its ability to support incremental writes, reduce model complexity, and lower operational costs.

Implementation details include designing wide tables that consolidate dozens of legacy models into a few entity‑centric models, splitting tasks to meet SLA requirements, and leveraging Hudi's update capability to improve data freshness and reduce storage/computation overhead.

Several concrete use cases are highlighted: (1) CDC data synchronization that shortens latency from 60‑90 minutes; (2) batch‑stream integration enabling real‑time scenarios such as the “Red Packet Rain” event; (3) data‑warehouse optimization that reduces model count from 71 to 3, cutting storage and compute costs.

The promotion strategy is described in four stages: validating functionality and scenarios, proving universal applicability, building a tool‑chain ecosystem to standardize usage, and encouraging broad adoption across business units.

A Q&A section addresses query‑speed optimization in Hudi, mentioning immediate read after write, merge‑on‑read, incremental change consumption, and secondary indexing.

Key lessons learned are summarized: demand‑driven development, institutional support for standards, and breaking departmental silos to harness collective intelligence, all of which contributed to the successful rollout of Hudi at Kuaishou.