Real-Time AI Lakehouse

A lakehouse architecture that ingests data as a **streaming first-class citizen** rather than as a periodic batch append. Built on a Log-Structured Merge-tree (LSM) layer over object storage (Apache Paimon is the reference implementation), the pattern unifies streaming writes from Apache Flink/Kafka, columnar storage on S3-compatible object stores, and analytical reads from Trino/StarRocks — all on the same physical table. Outputs Iceberg-compatible snapshots so analytical engines that don't natively speak Paimon read the same data without an ETL hop.

Traditional lakehouse table formats (Iceberg, Delta, Hudi) were designed primarily for batch ingestion, with streaming patched on later. AI workloads — agentic memory, real-time embedding pipelines, live-dashboard analytics over user interactions — generate continuous events at hundreds-of-thousands-to-millions of rows per second, exposing the write-amplification ceiling of copy-on-write formats. Real-Time AI Lakehouse architectures keep ingestion under sub-minute latency *and* keep analytical engines reading the same data via an Iceberg-shaped read surface.

Continuous CDC ingestion into the analytical layer for AI feature engineering, agentic-memory write paths where thousands of agents append context per second, real-time embedding pipelines feeding both training and online inference, unified streaming + batch analytics where the same table must serve both shapes.