Uber Launches IngestionNext: Streaming-First Data Lake Cuts Latency and Compute by 25%

Uber's IngestionNext represents a fundamental shift in how we should think about data freshness versus infrastructure cost. The 25% compute reduction is noteworthy, but the real win is collapsing ingestion latency from hours to minutes—this directly enables real-time feature engineering for ML models and operational dashboards without building separate fast-path systems. I've seen teams maintain parallel batch and streaming architectures for years, which doubles operational burden. What's significant here is that Uber built this on proven open-source foundations (Kafka, Flink, Hudi) rather than proprietary black boxes, meaning the architectural patterns are portable. For most enterprise teams, this signals that streaming-first data lakes are now table stakes, not cutting-edge experiments. The practical implication: if you're still designing around batch-window thinking, you're already behind on both latency and cost. My recommendation is to evaluate Apache Hudi for incremental updates in your existing data lake—it's the immediate unlock without rearchitecting everything tomorrow.